DR. NILESH MEHTA: Hello. Welcome to day five of the NIH Malnutrition in Clinical Settings,
Research Gaps and Opportunities Workshop. My name is Dr. Nilesh Mehta, I'm a professor
of Anesthesia Critical Care at Harvard Medical School and Faculty in the Critical Care Division
in the Department of Anesthesia at Boston Children's Hospital. On behalf of my co-chairs,
Dr. Gail Cresci, Dr. Todd Rice, Dr. Alison Steiber, and Dr. David Seres, and on behalf
of the NIH scientific leadership, Dr. Christopher Lynch, Dr. Charlotte Pratt, and Dr. Ashley
Vargas, It is my pleasure to welcome you to the second day of the early life malnutrition
sessions. We are grateful for this opportunity to contribute to this unique workshop, and
we are truly impressed by the foresight, time, investment, and commitment to malnutrition
research by the NIH scientific leadership. We are also grateful to Kimberly Barch for
keeping us organized over the past month, for Mark Dennis, for all the logistics
support, and special thanks to Brett Long, along with his colleagues, my colleague Christi
Jules, and Israel Gonzalez for their expert help with this digital platform Labroots during
all our sessions. Thank you to our expert speakers who have
generously given their time to present on a variety of topics over these two days. We
started day four yesterday with an introduction to early-life malnutrition. We discussed its
burden, the epidemiology, we talked about tools of screening, and concepts of diagnosing
malnutrition. We alluded to research considerations along each of these topics. We reviewed malnutrition
in preterm infants, neonates, infants undergoing cardiac surgery, children dependent on food
systems. We did some common themes that were alluded to in the first three days of this
workshop in the adult malnutrition topics. We summarize the evidence, current guidelines
pertaining to malnutrition. And in our final session, we highlighted disease-specific malnutrition
in select conditions. Not exhaustive, but very comprehensive. And in these conditions,
we identified gaps in research and look for potential advances for the future. After a
very full and exciting day one, we are once again fortunate to be joined by a group of
clinical experts, thought leaders, and research leaders in our field.
Once again, thank you to all of you who have joined us over the past five days of this
workshop and the last two days of the early Life malnutrition workshop sessions. We look
forward to our discussions during the day. I hope that you will consider during this
session to share your questions for the presenters. You can use the chat box on your screen and
if you send these in, I will collate and try to get to as many as we can during the Q&A
session. We have an exciting day ahead. So, let's get started once again with a warm welcome
to you all with Session nine. Session nine today is Early Life Malnutrition Research
Considerations Part two. In this session, we will first describe some updates in malnutrition-related
measures in pediatrics. To that end, Dr. Leanne Redman from Pennington Biomedical Research
Center in Baton Rouge, Louisiana, will speak on the assessment of energy needs, advances,
and future directions. This will be followed by an update on body composition assessment
in pediatrics by Ms. Stephanie Merlino Barr, who's in the neonatal intensive care unit
at MetroHealth Medical Center, Cleveland, Ohio.
This will be followed by a discussion, a presentation on pediatric malnutrition, specifically addressing
outcomes that we would want to measure, that we measure currently. This presentation will
be by Dr. Vijay Srinivasan, who's a faculty in critical care medicine at the Children's
Hospital of Philadelphia and University of Pennsylvania. Following this group of presentations,
we will then move to discussing disparities, family and system level, determinants of early
life, malnutrition, risk access, and treatment. In that session, in that group, we will first
be started on a discussion and presentation on impact of social determinants of health
other than food insecurity on low birth weight, growth faltering, and anemia among children.
This presentation will be given by Dr. Deborah Frank from Boston University. This will be
followed by Dr. Laurie Nommsen-Rivers from the University of Cincinnati, who will discuss
excess weight loss and failure to thrive in the context of exclusively breastfed newborns.
Once again, highlighting research gaps in prevention, identification and treatment,
and inequities. After all these talks, which will follow in tandem right after this introduction,
we will come back at 1pm for the Q&A session. I will be the moderator for the Q&A session
and I will be assisted by my colleague, Dr. Todd Rice, and our NIH program lead, Dr. Ashley
Vargas. After the Q&A session, we will take a short break and then move on to session
ten. So, once again, I hope that you will enjoy the presentations. I thank once again
all the expert speakers and I look forward to receiving your questions during the session
and see you on the other side for the Q&A session. So, let's kick this off now with
the first presentation, which will be on assessment of energy needs by Dr. Leanne Redman. Thank
you.
MS. STEPHANIE MERLINO BARR: Good afternoon. My name is Stephanie Merlino Barr. I am a registered
dietitian in the neonatal intensive care unit at MetroHealth Medical Center in Cleveland,
Ohio, and a PhD candidate in the Department of Population and Quantitative Health Sciences
at Case Western Reserve University's School of Medicine. Today, I will be speaking about
body composition assessment in pediatrics. My objectives today will be to identify methods
of body composition assessment and explore their validation in the literature focusing
on the infant population. I will describe the challenges of these measurements and their
implementation into the clinical environment. Body composition assessment in infants and
young children is challenging to perform due to rapid growth and substantial changes of
body composition that occur. Preterm infants have unique body composition development compared
to their term infant counterparts with lower lean body mass secretion, resulting in relatively
higher percent body fat at term corrected age.
Peak percent fat mass occurs at approximately six months of age before independent locomotion
begins and then slowly declines throughout early childhood. These distinct periods of
rapid growth are influenced by important variables, including sex, ethnicity and race, and disease
states. Understanding, body composition, development and utilizing its assessment as a part of
standard clinical care may allow for a better understanding of the relationship between
disease growth and nutrition, allowing for the promotion of optimal growth and health
outcomes through nutrition and non-nutritional strategies. The most clinically feasible tools
for body composition assessment are anthropometric measurements. There is not one measurement
that can fully divulge all there is to know about an individual's growth and body composition.
Accurate measurements obtained using the appropriate tools and assessed on standardized growth
charts are necessary for these data to be useful in a clinical setting. Weight, length
and head circumference are already used clinically. In premature infants, birth weight is predictive
of lean body mass at birth. However, weight on its own does not allow clinicians to assess
the quality of growth over time. Head circumference for age Z scores can help identify infants
at risk for long-term neurodevelopmental impairment as head circumference is indicative of brain
size. Smaller head circumferences have been observed in pediatric patients classified
as malnourished using this subjective global nutritional assessment and deviation from
the WHO growth standards. Head circumference is not as sensitive an indicator of short-term
nutritional status, as brain growth is protected in acute nutritional crises. The most important,
cost-effective, and clinically feasible measurement that I will discuss today is the crown-heel
length measurement on a recumbent length board. Unlike weight, linear growth is not influenced
by fluid status. In the infant population, linear growth has been associated with lean
body mass, protein accretion, and organ growth. Linear growth has proven to be a useful and
important marker of nutritional status in the management of infants with bronchopulmonary
dysplasia. For all my pediatric clinicians looking to improve your overall approach to
growth assessment, ensuring appropriate length using a length board is the place to start.
Other anthropometric measurements like mid-upper arm circumferences, and flank skin folds
are more frequently seen in research settings but have begun to be utilized in clinical
care. Mid-upper arm circumferences have been reliable predictors of fat mass in preterm
infants in pediatric populations. MUAC is not as influenced by fluid status as other
areas of the body and therefore can be well implemented in an ICU environment. Mid-upper
arm circumference growth trends for preterm infants in other pediatric populations have
been described over the past several decades by researchers, the CDC, and the WHO. However,
consensus on ideal mid-upper arm circumference growth in modern pediatric populations, standardization
of measurement tools, and the incorporation of MUAC growth charts into electronic medical
records are needed for clinical implementation. Skinfold measurements like (INAUDIBLE) skinfold
are performed with calipers and are most often incorporated into predictive equations of
infant body composition, which will be discussed shortly. Skinfold measurements are correlated
with fat and fat-free mass but have relatively low explanatory value compared to weight and
length. Proportionality indices like weight for length can be useful tools in a clinical
setting to illustrate and discuss proportional growth. However, in the pre-term infant and
pediatric populations, the utility of weight and length indices is inconsistent in predicting
fat and fat-free mass. Other indices not commonly used in a clinical setting include our muscle
area, which uses mid-upper arm circumference and triceps skinfold has been studied in older
children and has a fair correlation with fat mass. However, this calculation has not been
found to be a reliable predictor of fat or fat-free mass in neonates. The mid-upper arm
circumference to head circumference ratio has been suggested to be a useful tool for
body composition prediction but has not been validated.
Some of skinfolds was not shown to be a good estimate of fat mass in preterm infants as
compared to DEXA and ADP measurements. Finally, predictive equations utilizing anthropometric
measurements have been validated against body composition analyses. While utilization of
predictive equations may be a more feasible method of implementing measures of fat in
fat-free mass into clinical practice, these equations may have limited external validity.
Secondary body composition assessment using techniques that estimate four, three and two-compartment
models have primarily existed in the research realm. For techniques like MRI and isotope
dilution, this may be where they remain due to cost, scale, and general feasibility that
make them likely incompatible with the clinical setting, particularly for infants. Dual x-ray
absorptiometry or DEXA is the only assessment method that measures bone mineral content
and regional body composition estimates. However, a lack of standardization of this technique
and a functional limit of serial scanning are important shortcomings to recognize.
Quantitative nuclear magnetic resonance is a relatively new technology and human body
composition analysis that can be used from infancy to adulthood. qNMR has shown to give
a precise measurement of fat and fat-free mass, but it is largely cost-prohibitive and
requires further validation. Methods that seem most feasible to translate to clinical
practice include bioelectric impedance analysis, which can be used in predictive equations
and typically performed best in the populations they were validated in. BIA is a useful tool
for epidemiological settings but may have limited usefulness in clinical practice. Ultrasound
is a non-invasive method of body composition analysis that utilizes technology that hospitals
typically have, making it a strong candidate for use in clinical assessment. This technique
has been used in pre-term infant populations and was able to observe accretion rates in
adipose and muscle tissues and correlate accretion rates with nutritional interventions. However,
this method is yet to be fully validated in the pre-term infant population.
Air displacement plethysmography is a commonly used tool in infant body composition analysis.
ADP has the benefit of being noninvasive fast and does not require subjects to be sedated
or expose them to radiation. Body composition growth charts based on ADP measurements allow
for improved understanding and assessment of changes in body composition in infants.
While two machines exist so that this assessment is feasible in infants, children, and adults,
it is not been validated for children between six and 24 months. Body composition changes
are used in malnutrition, assessment, and diagnosis. Currently, this objective global
nutrition assessment utilizes a nutrition-focused physical examination to determine subcutaneous
fat and muscle loss to help identify malnutrition in classified level of severity. An excerpt
of the assessment is shown here. In a user reliability of the SGNA has been fair
in published research, highlighting both the need for trained professionals to perform
this assessment, as well as the opportunity for utilization of subjective body composition
markers and malnutrition assessment. Registered dietitians are uniquely qualified
clinical professionals to both perform this assessment and create a clinical nutrition
intervention based on its findings. To summarize the challenges of implementing body composition
assessment in the clinical setting, first, the cost of initial materials ranges between
approaches. Second, feasibility of implementation is driven by the setting, for example, inpatient
or outpatient, ICU versus stable patients. Next, performing these measurements requires
adequate staffing and training to ensure consistent quality in the measurements performed. Finally,
answering, so what? What can we do with this information when we obtain it? Body composition
assessment in infants and children is uniquely challenging due to the dynamic growth that
occurs during these phases. While many methods of body composition assessment exist, there
is not one perfect measurement that can currently be used for all patients in all settings.
Research priorities in this area are as follows. First, to validate body composition, methods,
and growth standards as there is a need for global cooperation and standardized data collection
processes and validation techniques. Second, to connect body composition changes with clinical
outcomes and interventions and premature infants. Research has focused on the relationship of
fat-free mass accretion with neurodevelopmental outcomes, the relationship of long-term metabolic
outcomes with early life body composition changes and growth has not been elucidated.
Determining clinical interventions to impact these long-term outcomes is needed. And finally,
to connect body composition changes throughout life phases as to better understand the complexity
of body composition development throughout the human lifecycle. Thank you so much for
your time.
DR. LEANNE REDMAN: Hi, everybody. I'm Dr. Leanne Redman, coming to you from Baton Rouge, Louisiana,
at the Pennington Biomedical Research Center. I would like to thank the planning committee
for the invitation to speak today. So, over the next few minutes, I'm gonna be talking
to you about the assessment of energy needs in pediatric populations, as well as some
advances and future directions that can apply to this field. So, as we know, malnutrition
or over and undernutrition reflects the perturbation to energy balance. And energy balance is simply
the relationship between energy intake and energy expenditure. There are three unique
states of energy imbalance. The first one being weight stability, or when the energy
intake or caloric intake is equivalent to the caloric or energy expenditure and the
body energy stores are stable. The second state of energy imbalance is a state of weight
gain or body energy store gain, and this occurs when the daily energy intake is sustained
at a much higher level than daily energy expenditure, and body energy stores are increased and weight
is gained. And the final state of energy imbalance is
a state of weight loss. And this occurs over time when the daily energy intake is sustained
at a level which is less than the daily energy expenditure. Body energy stores, as a result,
decrease to mobilize energy, and weight is lost. Of course, behind that very simplistic
view of energy balance is the far more complex physiology of metabolism and dietary intake.
So, on the energy intake side of this equation, of course, we need to consider the quality
of the diet, because not only does each macronutrient have different impact on overall total daily
energy intake. Macronutrients also have independent effects on body energy stores, so whether
or not calories would be preferentially restored as fat or as lean tissues. And we also know
that macronutrients play a role in total body water and they impact the energy expenditures
as well. So, similarly, on the energy expenditure side of the equation, there are a number of
different ways that people partition energy. So, the first is and the largest component
of someone's daily energy need is their basal metabolic rate or their resting metabolic
rate is also termed that this accounts for about 60% of daily energy needs. But in addition
to these, people are also expending calories through the digestion, processing, and absorption
of nutrients. Of course, physical activity, although this is the most variable component
of daily energy expenditures and it's divided into non-exercise expenditures or activities
of daily living as well as structured physical activity. And of course, we have energy expenditures
related to thermogenesis, which can be adaptations to perturbations in the diet, changes in the
thermo environment. And in addition to this, in children, we also have an energy cost associated
with growth. There are a number of different tools that we can use clinically to evaluate
energy needs in people and in pediatric populations. So I've summarized a suite of measures that
are available to us in terms of their level of accuracy, their burden to patients, as
well as to the clinics, the relative cost of conducting these types of procedures, and
therefore their current utility or dissemination, particularly for malnutrition and studies
in pediatric populations. I'm not gonna go through all of these, but
just to let you know that there are sort of two gold standards here, two gold standard
methods for evaluating energy needs and changes in energy balance in people. The first would
be doubly labeled water. It can be a tool that's used in both inpatient as well as outpatient
settings. It's considered the gold standard because the accuracy is between three and
5% for these tests. It's burdensome because these measures occur over a period of about
ten days. It measures isotope enrichment during that time period. So, therefore, that requires
specialized equipment that's not available in all places. The average cost of one of
these tests ranges depending on someone's body weight, but it could be in a pediatric
population as low as, say, $300 for an infant all the way up to $700 or more in an older-aged
adolescent. Of course, we can also directly assess energy needs clinically in a different
way, and that would be to measure basal metabolic rate. Already told you that was the largest
component of someone's energy expenditure. The second largest component is physical activity.
So, by doing a 30-minute assessment of someone's basal metabolic rate in the clinic, we can
multiply that by an activity factor to derive a direct measurement of energy needs. This
is not as good as using, say, the doubly labeled water, but it comes with less burden, it's
a shorter test. The accuracy is still pretty good and cost is not too extreme for clinics.
So, you'll find that this type of assessment is routinely used in a larger number of studies,
not only for children but also for adults. We can, of course, ask people to help us understand
their dietary energy needs and their current dietary intake using different types of recall
tools. This is obviously prone to quite a lot of bias. It's been studied a lot. It's
generally not recommended, but these tools still bring a lot of value for understanding
where those calories are coming from.
But finally, and thank goodness on the back of a large number of doubly labeled
water studies, as well as studies using resting metabolic rate to evaluate energy needs, we
now have a large number of prediction equations that have a fairly moderate and in some cases
high level of precision for estimating energy needs. These tools are low burden, low cost
and have been used quite widely, especially in clinical practice, but also in research.
So just to give you an example, here are some of the prediction equations that are published
in the dietary reference intakes from 2005. That was the last time that the DRI for Energy
were evaluated and published. But the National Academy of Medicine has convened a new panel,
and so these equations are currently being re-evaluated and we would expect a new publication
coming next year. But nevertheless, this is just to give you an example, this is an example
of one doubly labeled water study that was done at Baylor College of Medicine. This was
in infants from birth through to two years of life.
And it's these types of data that we use to inform these equations to estimate energy
requirements in children back in the 2005 DRI report, just as shown in this example,
these equations are specific to different age groups of children, which is very appropriate
because we have different rates of growth and different rates of weight gain that are
needed in different pediatric cohorts. So you can see that the babies are divided here
into a couple of different smaller cohorts by age. Once you get into older children,
that's just two cohorts. There's these two equations here for boys and girls now between
three and eight years and here on the next slide is the next set of equations, which
would be for children age from nine to 18 years and again sex-specific for boys and
girls. Just as I mentioned before, we have to account for physical activity here in these
estimates, because these estimates are estimating basal metabolic rate multiplied by the physical
activity factor, but widespread use because in clinical practice all we need is the individual's
age, the individual's weight and height, in order to derive these estimates.
But the field has advanced now. So now in the context of weight management, and I'd
say more specifically in adults, various groups, led by those such as Dr. Diana Thomas from
West Point or Dr. Kevin Hall from NIH,as shown here, have developed now what they call dynamic
energy balance models or models that can not only predict someone's energy needs or energy
requirements, but also how a change to energy balance can impact the rate of energy that's
being petitioned into fat-free mass and fat mass that is shown here on this slide. This
is an example of an energy balance model that has been developed by Kevin Hall's group in
children. These equations also now take into account the energy cost of growth. So the
little p here is indicating partitioning of energy. So in these equations, you can
estimate how differences is in energy from intake and expenditure and growth over time
can influence the amount of energy that then would be partitioned into the fat-free mass,
into the fat mass over time. So this is really exciting for the field because
Kevin Hall and others were able to leverage these large datasets of doubly labeled water
as well as body composition from various studies in children to not only develop these models
but to validate them as well. And just to give you an idea as to the consideration of
the model parameters that are behind the scenes so, behind that piece of the model that determined
energy partitioning into fat-free mass or of fat mass. You can see here in this chart
that these models are very, very sophisticated. They're taking into account growth, not only
overall growth but the growth that is occurring at different organs as well as tissues. They're
also being able to take into account the metabolic rates of these organs and tissues, like I
mentioned, as well as the impact of different levels of physical activity. So I want to
show you the utility of these dynamic energy balance models in action and really to emphasize
to you the value that this can bring to the field.
So I called Kevin one day and I said, "Kevin, I have a problem. I've been working on some
data using infant formula, and we have learned in our studies that about...that people are
over dispensing formula by about 11% per bottle that's being made. So if I would extrapolate
this over the total number of servings provided to babies in a day over by the number of days
in a week, and now I have an estimate of overfeeding. I want to know what this is going to do to
a baby's body weight over time. If that was a baby boy and a baby girl." And he's like,
"No problem, we can put this into our infant growth model." So, we worked on this problem
together. And so, we overlaid here this is the CDC growth chart for a baby boy (blue) and
a baby girl, it's pink. And assuming that the baby girl and baby boy is born at the 50th
percentile and they are overfed this number of calories per week, Kevin's model was able
to predict for us how the body weight would change, how that person would grow from birth
to six months, which was the period of our question.
So you can see here in this example, the baby boy who starts off with, you know, a weight
and length right at the 50th percentile all over feeding to this degree, based on this
dynamic model, showed us that this baby boy would have a weight Z score that would be
above the 70 percentile fall and very similar for the baby girl. The other thing that I
wanted to know was where is this energy being partitioned. That is then the next exciting
part of Kevin's model. So it's kind of being cut off here by my video. I apologize for
that. But we were able to then model what the changes would be in this hypothetical
boy and girl body fat stores as a result of the overfeeding. So you would see this would
be a baby girl that would be fed according to a regular goal, which would be to not have
overfeeding. This would be the body fat mass prediction in the baby girl that had the 11%
overfeeding and the same results here for the baby boy. So I wanted to show you as an
example of how you could utilize these models in future studies in this area.
And to give you now a flavor for a proof of concept with designing an intervention based
on these models. Similar to Kevin Hall, Diana Thomas she developed a mathematical model
that would predict the amount of calories a woman would need to eat in each trimester
of pregnancy to gain the recommended amount of weight that is predetermined by the Institute
of Medicine guidelines. And those are BMI specific for people in pregnancy. So I brought
this problem to Diana and she created this model and then we created this applet or online
tool where you can come to the tool. I can enter in a patient's age, her height, and
her weight. And the model will estimate for me the number of calories that this person
would need to eat in each trimester of pregnancy in order to gain weight within this Green
Zone. What's really neat about an applet like this is, as I mentioned, we can have specific
energy intake targets at different points in time. We can also as a function of time,
then clinically plot changes to the person's weight over time and see how well the person's
doing to adhering to these calorie goals based on the estimated changes in body weight.
We utilize this tool to drive a weight gain intervention. And this is again, an example
thats in pregnancy. It's a little different, but it can apply here into interventions say
refeeding in a failure to thrive type scenario. So in our study, we enrolled overweight and
obese pregnant women. We used this dynamic gestational weight gain model to provide women
with calorie goal estimates for each trimester of their pregnancy. The women received these
calorie goals, and they were coached to understand that their body energy stores or their weight
here in this case was really a reflection of whether they were hitting this daily energy
intake target. Of course, just like Kevin's model for the kids, this gestational weight
gain model also took into account maternal physical activity. So over the course of the
pregnancy, all the women were doing were charting their weight. They knew if their weight was
in this Green Zone that they were hitting their dietary intake target. On the back end
we programmed this into an app. Clinicians could see the weights coming in.
Health coaches were seeing the weights coming in. If the weight was out of the target zone,
we were able then to provide targeted and personalized feedback to the woman to help
tailor her dietary intake goals, as well as her dietary intake in general, to have the
weight falling within this weight gain zone. So I'm overlaying now the example here of
failure to thrive. Wouldn't it be awesome to be able to partner with people like Diana
and Kevin to derive not only static estimates of energy needs for some of these critical
pediatric problems that we're facing with malnutrition value to thrive as well as obesity,
we could design interventions that not only provide the energy requirement, but model
for us the changes in these people's fat mass, fat-free mass and growth over time, and provide
coaching in real-time in outpatients and deliver interventions. So I'm going to wrap up by
saying energy balance is dynamic. For decades, the field of nutrition has relied on energy
requirement estimations from static equations and from the combination of different methods.
But these new dynamic models of energy balance have improved our ability to prescribe precise
changes in calorie intake and to learn what are the expected changes not only to a person's
body weight but also to their body composition. In pediatric populations, models include important
energy partitioning for growth, as well as the resulting changes in energy partitioning
into the body's energy stores. I've showed you some examples that we can rely on these
models to guide treatment in certain clinical conditions such as pregnancy. And I think
that we could use models to guide treatment in conditions of malnutrition as well as with
childhood obesity and that we can create similar e-health tools to deliver timely advice to
patients and customized interventions. So I would like to give a shameless plug for
Pennington and my lab. I love to mentor. We are looking for postdocs.
DR. VIJAY SRINIVASAN: Hello, I am Vijay Srinivasan, and it's my pleasure to be with you today.
My presentation today will discuss the concepts of defining and measuring outcomes from pediatric
malnutrition. I have listed my disclosures here and do not have any conflicts of interest
relevant to this presentation. Though it may be tempting to think of discrete and measurable
outcomes directly attributable to malnutrition in early life, we are typically faced with
the complexities of several built-in risk factors and etiologies that are often intertwined
in the cause and effect pathway as highlighted in this figure here. Most of these risk factors
are often part of the malnutrition paradigm, a key point to be considered when we design
studies and interventions aimed at combating malnutrition in early life. Further, as these
risks accumulate, early child development is increasingly compromised. Over 5 million
children under the age of five die each year with these deaths occurring disproportionately
in low-income and middle-income countries. For many vulnerable children, the risk factors
of malnutrition, illness, lack of access to care, poor social support systems, and poverty
contribute to an unstable health trajectory with repeated episodes of illness interspersed
with short periods of incomplete recovery highlighted here. In many regions of the world,
acutely ill children may not even access health care during episodes of illness. Of those
that do studies have shown that children who are hospitalized have a dramatically higher
risk of death, both during the hospitalization as well as in the months following discharge
than their community peers. Even when guidelines for treatment and clinical follow-up are closely
adhered to. Thus contact with the health care system resulting in hospitalization serves
both as an indicator of vulnerability, as well as a time point where children are readily
accessible for intervention. Traditionally, pediatric malnutrition is defined as an imbalance
between nutrient requirements and intake that results in cumulative deficits of energy,
protein or micronutrients that may negatively affect growth, development, and other relevant
outcomes. In 2013, a new classification scheme was developed
by Aspen, led by Dr. Mehta, and consists of the five domains of chronicity etiology,
mechanisms of nutrient imbalance, severity of malnutrition, and outcomes. This more granular
and detailed classification scheme is an important step forward to better characterizing chronicity
and mechanism while laying a better framework to define and measure outcomes. There are
several key outcomes impacted by nutrient imbalances in addition to traditional anthropometric
parameters and biomarkers other outcomes affected by malnutrition include changes in the metabolome,
mortality, organ dysfunction, loss of skin integrity, greater ICU dependency with hospital
resource utilization. Immune dysregulation of acquired infections. Alterations in muscle
and bone function as well as cognition and development. All of these measurable outcomes
can ultimately influence the future trajectory of health, status, development, education
and socioeconomic status of the individual and the community as a whole.
Defining outcomes more clearly within the appropriate framework is therefore valuable
to help us advance greater evidence-based nutrition practices. I am now going into a
little bit of detail in some of these outcomes, which I think are relevant for us to understand.
Childhood sarcopenia has only been recently identified within the context of reduced skeletal
muscle mass. The similarities between Sarcopenia and malnutrition definitions include the overlapping
principles related to depleted lean body mass alterations in muscle functionality and suboptimal
nutrient intake leading to nutrition deficiencies. In children, this also translates to growth
failure with a potential neurodevelopmental delay in growth and fine motor development
and cognition. There is currently no gold standard tool to assess motor function impairment
in children being assessed for sarcopenia. It is challenging to assess muscle function
in a standardized way with infants and young children due to several factors, including
the development of postural control, coordination, core stability, and the ability to perform
purposeful in isolated movements. In older children that is school-aged children
or adolescents handgrip test and a six-minute walk test are well documented to determine
upper body strength and functional exercise performance respectively. Cognition and development
is another very vital area in which it is challenging to measure outcomes. The continuum
from the prenatal stages through the early to middle childhood years represents a critical
period for child neurodevelopment. Nutritional deficiencies, infection, and inflammation
are major contributors to impaired child neurodevelopment in these years. Malnutrition as highlighted
below can result from lack of both macro and micronutrients. Stunting in early childhood
is associated with poorer cognitive development and academic performance in later childhood.
Even mild but persistent malnutrition in early life that is during the first two years negatively
influences reasoning, visuospatial functions, IQ, language development, attention, learning,
and academic achievement. The majority of studies which have investigated
the association between nutrition and cognitive Development have focused on individual micronutrients,
including omega-three fatty acids, vitamin B12, folic acid, zinc, iron, and iodine. The
evidence is more consistent from observational studies which suggest that these micronutrients
play an important role in the cognitive development of children. However, the results from intervention
trials of simple nutrients are often inconsistent and inconclusive. We need better controlled
and more adequately powered studies. Additionally, children living in poor countries may encounter
more multiple micronutrient deficiencies as opposed to children living in richer countries
who are reasonably well nourished and where a small deficiency in one nutrient may not
result in measurable long-term change in cognitive outcomes due to compensation over time. These
are important considerations for us to keep in mind because nutrients do not act alone.
Rather, they are often synergistic in mechanism and in other contexts may even have antagonistic
effects with each other. There are several gaps that we need to account
for as we continue to explore how to define and measure outcomes related to malnutrition.
For example, how does timing, severity, frequency, and duration of nutrition deprivation impact
outcomes? What are the right scales, tools, and surveys to measure outcomes? What should
be the approach to account for confounders that are often found in lockstep with nutrition
and impact outcomes in very interrelated ways? The development of a common data element framework
to create a common language among pediatric nutrition researchers is an important first
step to establish precision, comparison, and reproducibility of findings. Additionally,
investing in registries and epistries supported by informatics applications may provide a
sound footing to study both short-term and long-term outcomes from nutrition evaluation
and interventions. I would like to conclude by emphasizing that early adversities associated
with severe poverty and chronic undernutrition contribute to the loss of development potential
and set children onto life course trajectories associated with negative health, educational,
psychological, and economic consequences that extended to many subsequent generations.
With the recognition that the foundations of adult health, wellness, economic capacity,
and well-being begin with experiences from conception to three years of age, the W.H.O.
has endorsed the promotion of child development during the first three years as a key strategy
for the success of United Nations Sustainable Development Goals. Thank you for your time
and attention. I look forward to our discussion to follow later.
DR. DEBORAH FRANK: [Inaudible] present...this information which comes from the research group, our Children's Health
Watch, in which I am involved to such a distinguished audience. The information is available online
at the website I will show you at the end of the talk. The aims of the presentation
are to summarize data regarding energy insecurity, housing instability, and cash transfers on
low birth weight, underweight in childhood, and anemia. I want to also summarize what
little we know about the impact of policy interventions in these domains on these indicators.
And to suggest some avenues for future research. Economic hardship acts upon children in the
first 2000 days of life through many mechanisms, including food insecurity, housing instability,
energy insecurity. These act reciprocally, each one exacerbating the other and converge
on the body of the baby. Most at risk are minoritized families, those with young children,
immigrant members, or children with special health care needs. They are most likely to
have difficulty in meeting their needs for housing and household energy.
I started out as a food insecurity and underweight researcher and was not interested at all in
these other dimensions until I showed this graph to the staff of the local food bank
because I couldn't interpret it in our emergency room for the three months after the coldest
months, the rates of children whose weight-for-age was less than the fifth percentile increased by 36%. I was
puzzled, but they weren't. They said that's heat or eat. Then, this observation from
the front lines compelled my colleagues to develop empirically a survey instrument to
identify levels of energy insecurity... Realizing from our own experience that inability to
keep houses and food cold can be as dangerous as inability to keep people warm. Moderately
energy insecure, meant receiving a threatened utility shut off in the last year and severe
energy insecurity refer to actual utility shut off at least one day with no energy for
heating or cooling or use of a cooking stove as a heating source in the last year. We found
that there was a dose relationship between these levels of energy and security and food
insecurity particularly at the child level, which has been shown in other studies to predict
growth faltering anemia. Whether efforts to mitigate energy insecurity
were effective, as indicated by our finding that children in families who participate
in the Low Income Home Energy Assistance Program, were less likely to have a weight-for-age less
than the fifth percentile and had a higher continuous weight for a Z score. Housing is
harder to measure, homelessness is a well established threat to child nutrition, but
there are many other dimensions of housing instability, including moving two or more
times in the past year, being behind on paying rent or mortgage or being threatened or actually
evicted. Housing instability dimensions and nutritional indicators in children vary, as
might be expected homelessness or its recent history, was related to low birth weight,
anemia, iron deficiency and referral for growth faltering. Multiple moves is tied to
low birth weight and lower average weight for age and eviction are threatened or actual
pregnancy is tied to low birth weight. Housing subsidies, have some impact within families
that are already food insecure among children from 2,764 food insecure families.
Those without housing subsidies compared to those with showed an adjusted odds ratio
of weight for age greater than two standard deviations below the mean. This is all that
we knew about targeted policy interventions. We then asked whether non-targeted subsidies
had any discernible impact on the risk of early undernutrition. The first was the Earned
Income Tax Credit, which is uniform at the federal level with various...in terms of state
generosity, and it was found that the level of EITC available to families per state was
reflected in the reduction of low birth weight across all racial ethnic groups. Whereas EITC
requires earned income and is given only once a year. During the COVID pandemic, the advanced
child tax credit was implemented which provided a monthly subsidy regardless of prior earnings
to families by the age and number of children. The census household pulse data which is obtained
online found that when the CTC began in July 2021, it was associated for the next few months
with a 26% reduction in food insufficiency, which is more severe than food insecurity
among households with children. The CTC monthly payments expired and we have
now been able to document a 25% increase in food insufficiency among households with children
through July 2022. The census household pulse survey has no clinical correlates available.
We have some take home messages. Energy and housing and security are correlated with food
insecurity often at the child food insecurity level, which in turn is a precursor of indicators
of malnutrition. Even after controlling for food insecurity, energy and housing insecurity
pose incremental risk to young children's growth and health. Most data is available
for low birth weight and there's real sparse postneonatal data, low income energy assistance
and housing subsidies are associated with improved weight for age, and young children
and cash transfers associated with produce low birth weight. Cash transfers are also
associated with decreased food insufficiency, which increases on transfer searches continued.
We need to inform our future research with guidance from those who have lived experience
both quantitative and qualitative. We should focus on sentinel groups. We need
clinical correlation to national surveys, monitoring nutritional status to identify
the impact in the first 2,000 days of fluctuating environmental and political conditions such
as heat waves, or anti immigrant rhetoric accelerating evictions. There are natural
experiments which exacerbate or mitigate hardships, simultaneously in the context of each other
rather than isolation. There will be new electronic health records innovations which mandate in
documentation of housing and food insecurity in adult inpatient populations but no other
I would suggest using our research to prioritize policy relevance. Prenatal and child malnutrition
correlates and material hardships other than food security are understudied. There are
measurement tools but not uniform. Early electronic health record measures of hardship should
be expanded to include obstetric and pediatric populations. Important for planning intervention
to disentangle which effects are mediated by food insecurity and nutrition benefits
which are identified incremental to those factors.
And we need to identify on clinical indicators of child malnutrition policies that broadly
support or reduce family incomes versus those which are targeted to a specific hardship.
Remember, the public policies and economic conditions are written on the bodies and brains
of babies. And please feel free to be in touch with me or go to the website for more information.
Thank you.
DR. LAURIE NOMMSEN-RIVERS: Good day listeners. In this brief talk, I will provide an overview
of excess weight loss in the context of the exclusively breastfeeding newborn. Today's
talk will focus on the scope of the problem, limitations of current knowledge and research
priorities across the translational research continuum. Exclusive breastfeeding is recommended
by all major public health organizations for the first six months of life. While most US
infants start off breastfeeding, the drop off is steep and exclusive breastfeeding rates
are quite low. One of the two healthy people 2030 goals for breastfeeding is to increase
the rate of exclusive breastfeeding at six months, from a baseline of 24.9% to 42.4%.
Thus, promotion of exclusive breastfeeding is a public health priority. It is universally
recognized that newborns lose weight in the first few days of life for exclusively breastfeeding
newborns initially infant feeding volume is very small. However, sometime in the first
few days postpartum milk production ramps up, we see a parallel increase in infant weight.
Note that typically infants are still on the downward trajectory with their weight at the
time of the birth hospital discharge. Despite weight loss being a normal adjustment to extra
uterine life, sometimes weight loss is excessive or never rebounds with potentially life threatening
sequela. Thus, there is a fine balance between avoiding unnecessary supplementation and avoiding
readmission and health consequences due to breastfeeding failure. You may be thinking
why not just give all newborns some formula as a bridge to avoid excess weight loss? There
are several reasons for avoiding formula supplement, one of which is the clearly established negative
impact it has on sustaining breastfeeding. In the study shown here, we found the adjusted
risk of stopping breastfeeding altogether to be over three fold higher if formula was
used during the birth hospitalization. And these results were in mothers with intention
to exclusively breastfeed. Nonetheless, there are serious risks to the newborn when weight
loss is excessive, including complications such as hypoglycemia, jaundice, dehydration
and hypernatremia. And if left undetected excess weight loss
can lead to seizures, renal failure, or hypovolemic shock. When it comes to newborn weight loss,
unfortunately U.S. babies are the biggest losers. In two separate cohort studies with hundreds
of newborns, we observed greater newborn weight loss as compared to the growth velocity data
from the WHO growth reference study. The median WHO baby is already surpassing birth weight
at day seven. U.S. babies on average have gained three to five ounces less weight in the first
week of life, with 16% of babies in Davis, California, and 18% of babies in Sacramento,
California losing 10% or more of their birth weight. And what does it mean to lose 10%
or more birth weight? In this systematic review, the authors compiled the admission weight
and serum sodium concentration of newborns readmitted for breastfeeding associated hypernatremia.
All had dangerously elevated serum sodium. 96% have weight loss greater than or equal
to 10% of birth weight. Most clinicians see weight loss of 10% or greater as a genuine
concern and to be avoided. Throughout this talk when I refer to excess
weight loss, I'm using the cut off of 10% weight loss or more. The newborn weight loss
tool or Newt was developed to improve tracking of newborn weight trajectories based on their
birth hospitalization rates. The Newt can be used to characterize current weight loss
relative to a large database of U.S. newborns weighed during their birth hospitalization.
That context is important. The orange line represents the 90th percentile, which for
vaginally born infants is consistent with more than 10% weight loss, suggesting an at
risk trajectory. Even though the Newt is useful for characterizing current weight loss relative
to other U.S. newborns, the data are entirely based on one to two weights per infant during
their birth hospitalization. And this raises the question as to whether the Newt is valid
as a predictive tool. Thus, we use data from the Davis site of the WHO growth reference
study to test the validity of the Newt in predicting excess weight loss once discharged
to home. Unfortunately, we found the Newt to be a very
poor predictor of excess weight loss. Only 21% of true cases of excess weight loss that
were detected on home visits were in the at risk range based on the Newt trajectory from
the hospital weights. Plus, in a recent randomized trial using the Newt as a clinical aid in
the decision to supplement, there was no difference in hospital readmissions being about three
and a half percent of exclusively breastfed infants readmitted in both groups. You may
be wondering why birth hospitalization weights are a poor predictor of post discharge weights.
Winnicott once said, "There is no such thing as a baby. If you set out to describe a baby,
you will find you're describing a baby and someone." In other words, we need to view weight
loss risk factors in the context of the breastfeeding DYAD both maternal risk factors and infant
risk factors. While the first few days of weight loss are related to infant factors,
once discharged to home maternal factors play a big role in newborn weight.
For example, based on the data site of the WHO growth reference study, the risk of excess
weight loss was nearly seven fold greater when the mother experienced delayed onset
of lactogenesis of more than 72 hours from birth. We saw a similar pattern in our Sacramento
cohort a first time mother infant DYAD. Unfortunately, delayed lactogenesis is surprisingly common
in the U.S. in the Sacramento cohort, which was entirely comprised of first time mothers
44% experienced delayed lactogenesis. Keep in mind that common doesn't necessarily mean
normal for our species. When we look across cultures, we see tremendous variation in the
timing of lactogenesis where some cultures commonly experiencing lactogenesis within
the first 24 hours of birth. Many factors such as hospital routines and maternal obesity
negatively influenced the timing of lactogenesis in the U.S. The bottom line is we have a situation
in the U.S. where exclusive breastfeeding is the public health goal. But we are operating
in an environment where there are many risk factors for delayed Lactogenesis and lactation
problems. Situation is magnified by our very weak safety
net for supporting the breastfeeding DYAD once they are discharged to home. To summarize,
I would like to apply a translational research framework that was developed for science and
human lactation and infant feeding. I led a workgroup in the development of this framework
as part of the NIH begin project. Our report is currently under review. When it comes to
access newborn weight loss, research needs span the translational research continuum.
At the T1 discovery stage, there is a need to elucidate the underlying drivers of delayed
lactogenesis and insufficient milk production. At the T2 human health implication stage,
we need to better characterize newborn weight change patterns, and their relation to both
healthy and unhealthy clinical outcomes across diverse groups. At the T3 clinical implication
stage, we need to develop effective interventions to support timely onset of lactogenesis, abundant
milk production, and healthy newborn weight gain.
We also need to develop and validate culturally appropriate screening tools to identify DYADs
at high priority for close post discharge follow up. At the T4 implementation stage,
we need to develop implementation strategies that ensure equity and access to optimal breastfeeding
support during the birth hospitalization, and post discharge. There is also a need for
educational interventions targeting health care providers, and also interventions targeting
education to parents. At the T5 impact stage, we need surveillance of weight change patterns,
readmission rates, and severity of hypernatremia in exclusively breastfeeding newborns. And
importantly, it is essential to monitor disparities in access to support for exclusive breastfeeding
both during and after the birth hospitalization, and to monitor disparities in efforts at prevention
and treatment of excess newborn weight loss. So thank you for your time today and I leave
you with a list of key references that were the foundation for this talk.
DR. NILESH MEHTA: Hello, welcome back to the Q&A session. Thank you so much to all our wonderful speakers.
This was indeed a very informative session. And it has already stimulated a lot of questions.
To everyone participating, please consider sending in your questions using the box on
your screen. We'll try to get to as many as we can. Let's get this off. I have
[inaudible], Dr. Ashley Vargas, in addition to all the speakers here. And I'd like to start with
our first speaker, Dr. Barr, Stephanie Barr, this question is for you. Could you comment
on the inherent limitations of predictive equations for populations related to race,
age, sex and secular trends over time? And how these are revalidated over time? Are there
any strategies to get these data at regular intervals to revalidate?
DR. STEPHANIE BARR: I think that's a great question. And as indicated, there are inherent
limitations of these predictive equations with the factors that were mentioned. And
Sharon Assadi and group in I believe New Zealand had a really nice paper that is referenced
in the slides for when you all can take a closer look at them that explored this specifically.
And I think it's important to know that, as referenced, there are equations that exist
and I think they could be useful tools in specific clinical settings, but they're definitely
not perfect. In terms of strategies on how to move forward, I believe that what needs
to be done in order to going off of what Laurie was discussing with moving things through
the translational research spectrum, there needs to be continued greater collaboration
outside as little individual research bubbles to get a better understanding of typical growth
patterns at a population level.
DR. NILESH MEHTA: Thank you. Yeah, it's absolutely true. On a similar reign, Dr. Redman next question
is for you. This is from Laurie Bishop from Boston, how might the energy balance model
be adjusted to predict energy requirements in children undergoing surgery or who are
hospitalized for acute illness?
DR. LEANNE REDMAN: Yeah, that's an excellent question. So and it's an important distinction because
the dynamic models that I shared from Kevin Hall, rely mostly on information coming from
healthy children. But there are a large number of studies and data available, say from CHOP,
right, Virginia Stallings has done a lot of work on assessing energy needs in critically
ill babies and young children. Those types of data can be used to make very specific
models for specific sets of medical conditions and patient populations. And to the point
of another question, which mentioned that there are fairly substantial changes that
occur over time in a critically ill child's resting metabolic rate, which would need to
be taken into account over time. So as long as the models are developed and validated
on populations that were intending to target, they should be able to do a good job and they
should definitely be able to be detested in inpatient type situations as well as outpatient
settings in the future.
DR. NILESH MEHTA: Thank you, Dr. Redman. The next question is for Dr. Srinivasan. Dr. Srinivasan, what
nutrients are nutrient combinations would you prioritize, to study for their impact
on neurodevelopmental outcomes in pediatrics?
DR. VIJAY SRINIVASAN: Thank you for that question. Gosh, I wish I could narrow it down. There
are so many of interest and choices here. But I think if I were to sort of think about
what are some of the key nutrients that impact in that continuum that we are focused on right
now, definitely the essential fatty acids, omega three fatty acids, still a big area
of interest in terms of how they impact neurocognition and development. And in addition, you know,
iron is, as in my opinion, still an important nutrient that is there are deficiencies worldwide
and I think it is such an important yet often overlooked in some ways nutrient. In addition
to the B vitamins as well. I would also add zinc, which is again another important component
in this and...
and finally, in addition to just the nutrients, it's even just, you know, looking
at the dietary composition, the quality of food and, you know, the timing of food, I
mean, there's data emerging, you know, the intervals at which one eats food in these
early childhood days beyond infancy can impact. So definitely more questions than answers,
but these are some of the key nutrients that I would like to sort of recommend that be studied.
DR. NILESH MEHTA: Thanks, Dr. Srinivasan. The next question is for Dr. Frank. Dr. Frank, you gave a terrific
overview of the social determinants and food insecurity and how they relate to malnutrition.
Could you say a little more about any research or literature out there on the effects of
local, state, or federal policies and health outcomes in chronically ill children? We have
DR. DEBORAH FRANK: We have looked at that in our cohort, which is a exclusively low-income pediatric cohort seeking health
care of kids under the age of four. We found that kids with special health care needs are
at higher risk of food insecurity and housing insecurity, families, and that SSI in both cases
has a (INAUDIBLE), which means the child is sicker than your obvious kid with special
health care needs and is very hard to get. But those kids are less likely to have food
insecurity at the child level while they still have it...the family still has food insecurity
compared to kids without special health care needs and are also less likely to be housing
insecure. I haven't looked at...It's a good question about energy insecurity. States vary
in the degree to which doctors can prevent families from having their power cut off if
they have a special health care-need child in the house. And that would be a very good
thing to look at. But that is clearly...many of the outcomes we look at like for age, hospitalization,
and so on are already increased from the special health care needs. And that so you have a
different baseline when you're saying, OK, so what are the health effects?
DR. NILESH MEHTA: Thank you. Thank you for that. Dr. Nommsen-Rivers, have you seen any clever examples of research,
any good research designs that are able to address questions across the translational
spectrum, especially in respect to the risk of malnutrition in infants?
[pause]
You might be muted. [pause] I don't think we can hear you. There seems to be some trouble with your
audio. We'll come back to you. Let's jump to...along the themes of some of these ideas
for research for the future, there are some questions related to what technologies out
there might facilitate some of this work. Dr. Srinivasan, you talked about outcomes.
Traditionally, particularly the long-term outcomes have relied on follow-up clinics
and therefore difficult to capture in a large country with referral work and mobility of
populations. Are there any cognitive functional outcomes or other outcomes in children that
are validated for remote data gathering that might facilitate getting these at regular intervals?
DR. VIJAY SRINIVASAN: Yeah, that's a great question. And I think you've really, you know, documented
or identified this really key area that's a problem because most studies start off strong,
and then follow-up becomes challenging in the long term, which is actually probably
the most useful information that we all need, particularly in terms of developmental and
neurocognitive outcomes. There are definitely tools that are available
at different stages of life. For example, in infants and toddlers, you could use the
Bayley scales, and then, you know, in older children as well, you know, you could use
the Wechsler scales. Parent and self-reported measures such as the ages and stages questionnaire
as well as the Vineland adaptive behavior scales have been used in clinical trials.
The important key points in this to keep in mind are one, we need trained data gatherers.
Two, we need to also make sure parents have access to be able to do this. And again, that
sort of can create a source of bias because many of these parents that are accessible
to do remote follow-up may also be the ones that, you know, have preferentially have Internet
and or cell phone connectivity or other sorts of, you know, features that make them different
from the population of interest that may not have access to such things. So one has to
be careful in how we sort of attempt to do follow-up. I will conclude by saying that
another novel method of trying to do follow-up is the use of remote vehicles.
If one were to bring the lab or one were to bring imaging or other, you know, the analysts
sort of say to the patient's home, that is certainly something that is also being explored
in remote communities and poses a great, you know, sort of opportunity to track more long term data.
DR. NILESH MEHTA: That's a great idea. I know that some of our participants I know Lori Bechard
in particular, has participated in remote measurement of body composition and energy
assessments in patients who are technology dependent. I also appreciate what you said
about being careful when you employ some of these technologies in terms of excluding a
certain percent of the population that may not have access to them. That's a very valid
and extremely important piece that the institutional review boards, the IRBs will definitely question
look at for these research. Thank you for that. We might have Dr. Nommsen-Rivers' audio
back. And if you do, we'll get back to the question of are there any examples of good
translational research in malnutrition in infants.
DR. LAURIE NOMMSEN-RIVERS: Well, yes. Can you hear me now? Does it sound like I'm back?
DR. NILESH MEHTA: Yes, you're back.
DR. LAURIE NOMMSEN-RIVERS: Yay, OK. The answer was to refresh my presentation for some reason. Well, I think the clever examples are we're,
again, looking at the diet. If we take the newborn excess weight loss tool, for example,
the Newt all of the inputs on that are infant focused. It's the type of birth, the sex,
age of the infant, and without...I don't know if everyone else is hearing rumbling, I'm
hearing some odd audio rumbling. And so I think where we need to go with future research
in the realm of preventing excess weight loss in exclusively breastfed newborns is that
we are collecting data on the DYAD. You know, a lot of times we are mining the electronic
medical record for our research productivity and how can we create future electronic medical
records that link the mother's record with the infant so that we can holistically look
at risk factors and protective factors and not just look at the infant individually.
And, you know, within this translational research space, I also think it's important that we
look at where was there a concern of insufficient breastmilk intake.
But these babies aren't showing up as readmissions because of the mother not able to get any
help with breastfeeding and turning to infant formula and stopping breastfeeding. So the
ones that end up readmitted really just represent the tip of the iceberg and they tend to represent
disproportionately white, upper-middle-class families. And whereas, other families are
much more quick to introduce formula, they don't have, you know, their context is very
different when it comes to how they're approaching this. So looking at socioeconomic differences
and how we're approaching insufficient breast milk intake is going to be important too.
DR. NILESH MEHTA: Thank you, Dr. Nommsen-Rivers. Thank you for that. You mentioned electronic health records.
I just want to put in a plug for our participants who are listening. After the break when we
finish this Q&A, we come back for the second session and it's exciting one. In Session
10, we will have Documenting Nutrition in EHR, the Electronic Health Records by Robert
Grundmeier and then followed by a pretty comprehensive on guideline development and
research design by Liam McKeever. So, do consider joining for that. But let's
get back to the questions. This question from Dr. Vargas is for all of you, and it addresses
one of the common conundrums for research related to micronutrients in particular. Blood
volume is a big limitation in evaluating micronutrient deficiencies, especially in young kids. And
there are limitations that are not necessarily a limit in adults. Are there any novel developments
in technology that you have seen in your areas that are promising, where you can limit the
burden of bloodletting in malnutrition risk assessment in kids?
MS. STEPHANIE MERLINO BARR: I'm happy to kick us off. [crosstalk] So as I mentioned, I'm a registered dietitian in NICU. So this is
my life. And I wouldn't say that it's necessarily a novel development, but I'll definitely put
in a plug for my profession where from a nutrition intervention perspective, having experts in
the field actively participating in multidisciplinary care is critical. And then in terms of actually
assessing malnutrition. I mentioned this quite briefly in my presentation
about utilizing skills that registered dietitians and nutritionists have like the nutrition-focused
physical examination, which specifically we have training on identifying signs of micronutrient
deficiencies. And this physical examination are one tool that is certainly less invasive
than a blood draw, which may not be feasible for my 600 grandbabies upstairs.
DR. NILESH MEHTA: Thank you. Anyone else want to jump in?
DR. LAURIE NOMMSEN-RIVERS: Well, I'll jump in from a macroscopic perspective. This is not my area of expertise at all, but I am dipping my toe into the machine learning space.
And absolutely machine learning needs to be paired with experts as Stephanie is talking
about. You know, you want to pair with the nutrition expert so you can understand what's
going on. But I think we have a lot of opportunity with looking at clusters of features that
may help us to understand in a more macroscopic way how, you know, what might be some effect
of ratios that we wouldn't think of from just, you know, plugging away one at a time and looking at that.
DR. NILESH MEHTA: Yeah. I endorse these views as well. I agree.
I think the context is also important, as you rightly said, you know, what is the setting?
Is it a critically ill child? Is it a child on parenteral nutrition? Is this, you know,
some other unique disease, unique therapies that might interfere with metabolism? And
I think if one were to utilize the context appropriately, I completely agree. I think one can be more targeted and specific in the approach.
DR. LEANNE REDMAN: And I will say that the ways in
which we've gotten around some of this in our research studies is by using dried blood
spots. So just with a simple fingerstick or heel stick, you can collect dry blood on the
filter paper. And then now there are a large number of assays that are available using
this technology. So I'm certain that many of the micronutrients could be evaluated in
this way. So it's brought a low burden assessment with a lot of possibilities into clinical research.
DR. NILESH MEHTA: That's a great point. Of course, we haven't even alluded to the fact that micronutrient
levels in blood, especially in my population, hospitalized, sick children are so unreliable
and the need to use functional measures of micronutrient deficiency and it becomes even
more relevant in that population. But thank you. These are great thoughts. There
is a question from Dr. Stevens from Columbia University for Dr. Redman, and this is
an interesting one related to energy balance. Dr. Redman, in sick children, are there
prospective randomized data to validate that feeding based on some measure of assessing
energy requirement like predictive equation has better outcomes? And can you randomize
other data that children would do better to randomized to minimizing caloric deficit or to overfeeding for growth?
DR. LEANNE REDMAN: Yeah, I don't know about these studies, but I know with the dietitian
in the room that these predictive equations are used all the time, at least as the starting
point to define feeding plans for critically ill babies. Then the energy balance part of
it, right? Weight is often tracked in the NICU over time. And the dietitians use the
weight then to titrate the calories up or down to get the desired result that they're
after. I don't know the extent to which these types of data have been published.
And I don't know whether or not these things have been done in a randomized controlled
fashion, but we certainly should be pulling the EMR data, which we'll hear in the next segment to be able to look at some of this. Yeah.
DR. DEBORAH FRANK: This is Deborah Frank. As a clinician,
there is a real urgency to get kids somehow repleted for their immune function. And that
versus the long-term risks of too rapid catch-up growth is a real dilemma, particularly because
these kids go up and down in terms of their infectious burden and then their weight loss and we start over and so on.
DR. NILESH MEHTA: Great point. Yes. Well, while we are on the theme of any
future technologies to assist some of this research, Dr. Redman, on the topic of
energy assessment, particularly the role, the burden of physical activity, have you
seen any data on accelerometers? I've heard a lot about that application to accurately
try to gather data on physical activity. But is the technology now reliable?
DR. LEANNE REDMAN: This is an excellent question because so many households now have different wearable devices.
And one thing that we don't want parents to do is to put this on the wrist of their child
and try to assess for themselves, you know, the amount of calories that the device is
saying and equate that to a truth. What we know from accelerometry research, right, these
are research-grade devices. They're very good at measuring steps, very good at estimating
the number of minutes per day that you are being physically active, but they do a very
poor job in estimating physical activity, energy expenditure, or estimating total number
of calories burned throughout the day. So in our studies, actually, we provide people
a pedometer if we want to be coaching on physical activity because we only want them to ever
see step information and not to try to give themself any pleasure in knowing that they
have burned 2,000 calories today. Like if you go and do a workout class and reward themselves
with something, especially for our people on like a weight loss thing, but these can
be really detrimental to what you're trying to do in clinical care.
So to answer the question, they're good for what they're designed to do, which is just
to track the number of minutes we're being physically active. They're very good for the
steps. Research grade ones are much better for us to understand more granular things
about physical activity, but not energy expenditure. DR. NILESH MEHTA: Thank you. I believe there is an explosion
of these devices, so there is a tendency to try to use them. So hopefully in the near
future, some good validation studies will help. The next question is for Stephanie Barr.
You gave a wonderful overview of various methodologies. This question from Lori Bechard, what body
composition methodology would you prioritize for validation studies, particularly which
one has the promise to best capture acute changes in the clinical setting for kids?
MS. STEPHANIE MERLINO BARR: Such a great question, and I'll answer with the caveat that my perspective is really in
the NICU population. So my answer for that population is different from what you might
say in an outpatient perspective or even in an older pediatric population.
In my research, I'm specifically interested in using air displacement plethysmography
and utilizing that machine in a clinical setting, which we have been doing here at my hospital,
MetroHealth, which is the county hospital in Cleveland, Ohio for the past several years,
have been collaborating with the group at Cincinnati Children's who is also implementing
this device in a clinical mode and looking for collaborators externally as well. So if
you're out there and you have an ADP machine, please do reach out. I'd love to know who
you are and what you're doing with it. There are, of course, pretty big limitations with
the specific device that is on the market for the NICU population in that you need to
be clinically stable in order to be measured in it. So, a secondary goal is looking at
what other measurements or modes of assessment can we use for infants with bronchopulmonary
dysplasia, who are really some of the kiddos we're most interested in knowing what body
composition development is like. So using things like we're trying mid-upper
arm circumferences, we have some flank skin fold measurements that are being done in our unit.
I know the group in Minnesota has looked at ultrasonography for body composition assessment.
I think all of these are interesting, but still with very big question marks on how
we can use these and specifically for validation purposes.
DR. NILESH MEHTA: Thanks so much. That's very helpful.I know we are coming close to the end of this session. I could squeeze in one very quick
question for Dr. Srinivasan, and you mentioned registries epistry based and I know some other
research areas trauma have done well with this and national registries in certain countries
in Europe have worked amazing for a large country like U.S. mobile populations larger
capture. Any thoughts on how one would develop a registry for malnutrition?
DR. VIJAY SRINIVASAN: What a great question. Thank you. I think it's a tough question. And I think COVID gave us many examples
of what to do well and what not to do well. And I think we may be helped by those instances.
I think key challenges with registries, while they are very useful to sort of have population-based
data, is the disparate populations across the 50 different states. In some ways, individual
states might be able to take the lead and, you know, develop their local sort of systems
that works better for their citizens. And it's tough because one has to contend with
transparency. I think there is always the fear of patient-level privacy and data concerns.
There is always the fear of how much does government get involved. On the other hand,
if the data is readily available to the participants and it can inform their future, that might
actually be appealing. So it's about leveraging the data that's there and not letting it be
trapped, but actually making it available perhaps in real-time or near real-time. And
these are some of the challenges that we hopefully can, you know, I think, overcome or at least
work towards. But to conclude, I would really like to say, in the moment, I think using
scientific networks through the NIH, Aspen and other large societies and organizations
may be the way to go to still use the trust that we have to build these networks.
Well said. Well said. Thank you for that. Looks like it's one...past 1:30. That brings
us to the end of session nine. My sincere thanks to all our expert speakers. Thank you
for a very stimulating discussion. Thank you to all the participants for joining, particularly
all the questions. Keep them coming. At this stage, we take a 15-minute break. And as I
mentioned earlier, we come back at 1:45, which is Eastern Standard Time. So 15 minutes from
now for session 10. And session 10 promises to be equally engaging, exciting. We will
have electronic health records. We'll have a discussion on guidelines and study design.
And then in the Q&A stage session, we'll focus on study design considerations. And we'll
be joined in addition to the speakers by our panelists (INAUDIBLE), Sherry Kirk and Julianne
Patterson. So enjoy the 15-minute break and I hope to see all of you back for session
ten. Bye-bye.
ASHLEY VARGAS: Hello and welcome everyone to session ten on Early Life Malnutrition
Moving Towards a Unified Approach. I'm Dr. Ashley Vargas. I'm a program director at NICHD.
And I'll provide the brief introduction for this session. I'm joined by Dr. Nilesh Mehta
and Dr. Gail Cresci. Dr. Mehta will serve as your primary moderator for the end of this
session. I'm really delighted to bring to you all today some wonderful talks. We start
with Bob Grundmeier of Children's Hospital of Philadelphia, where he will focus on documentation
of nutrition and the electronic health records. Followed by Dr. Liam McKeever from Aspen who
will focus on the support needed for better pediatric malnutrition guidelines. After this,
we'll pause for questions briefly then move into a panel session. This panel session features
Dr. Wendy Sue Swanson from SpoonfulOne and Stanford. Dr. Shelley Kirk from Cincinnati
Children's Hospital Medical Center, and Dr. Julie Ann Patterson from Northern Illinois
University. All three of these individuals come from slightly different areas not directly
involved in childhood malnutrition research. But what they do bring to this panel that
we're very excited about is experienced doing some new novel applied methods for clinical
trials, data collection, registries, etc. Maybe some new tools that we can use in the
malnutrition space to help move this research forward. So that panel session will be moderated
by Dr. Mehta. During the session, please remember to share all your questions for the presenters
and panelists in the chat box. And we'll try to address as many as we can during the panel
discussion. Without further ado, we'll move into the presentations. Thank you.
ROBERT W. GRUNDMEIER: Hi, my name is Bob Grundmeier and I am happy to be here today for this opportunity
to talk about documenting nutrition in the electronic health record. My focus for this
talk will primarily be on the primary care pediatrician's perspective on collecting nutrition
information in the electronic health record. Over the course of my remarks, I will discuss
information that is readily available in the electronic health record. I will talk about
other information that is available, but might require some extra effort to be able to extract.
And I will conclude with some thoughts on how we might improve data collection regarding
nutritional status as part of routine care in the future. One of the most useful types
of information that is almost universally available in the electronic health record
is information about child growth. But unfortunately, as you might imagine, there are some inaccuracies
in measurements that as part of routine care, and some adjustments must be made to account
for that. What's shown on this figure is an example
of a sample of about 100 growth charts overlaid on top of each other, showing how there can
often be implausible measurements. We see the measurements sort of jumping up and down
unexpectedly and implausibly. This slide shows the same information after it has been cleaned
using an algorithm that I had the pleasure to be involved with the development of with
my colleague Dr. Daymont. The citation is shown there for your reference. And in this slide
we see that there is implausible measurements that were in the routinely collected data
have been eliminated. Flipping back and forth between the screens, we can see how nicely
the algorithm gets rid of the implausible measurements while retaining those measurements
that are likely reflective of the child's growth. Laboratory data is another kind of
information that is easy to extract from the electronic health record. And in some cases,
we have useful lab data that is collected on virtually all children depending on their
age. For example, there are recommendations to
collect hemoglobin or complete blood counts at particular ages, such as in infancy and
in the second year of life, and typically an additional check for anemia in adolescence,
especially amongst girls of menstruating age, there's also guidance to check cholesterol
at particular ages. And this is also routinely captured typically in the tween years as well
as shortly before transition to adulthood. Some other labs are periodically collected
in particular situations that might be useful from a nutrition standpoint. For example,
vitamin D, glucose, and liver enzymes are routinely collected for children who may have
overweight status. And even less frequently, but also useful, there are labs that may be
collected specifically among children with poor growth. This can include comprehensive
metabolic panels, which typically includes nutritional measures such as albumin. There
are inflammatory measures that we often collect such as C-reactive protein or erythrocyte
sedimentation rate as well as occasionally thyroid status.
But these are not collected routinely, only in particular cases where there are concerns
about poor growth. Now, I would like to turn our attention to some types of information
that may be particularly useful, but are a little more challenging to use because the
information is often in free text. Pediatricians collect information about child nutrition
status at basically all preventive health care visits, but they do so in a wide variety
of different ways. I searched through the templates that were available in an electronic
health record vendor product that is shared across many health systems and identified
that there were 307 different structured templates, meaning sort of checkbox based templates that
are used across 19 different pediatric health systems. Looking within my own health system
at Children's Hospital of Philadelphia, I identified that we have 25 different structured
templates which allow you to pick responses from lists, for example. And in addition,
there are 617 at least free text documentation templates.
This does not include the nutrition assessments that might be embedded in the context of other
visits such as preventive health care or other visit templates. Here is an example of one
such template at CHOP. This one is related to breastfeeding and generally allows clinicians
to type in whatever they want and allows flexibility. In this particular screen, the three asterisks
indicate a field where the clinician can really type in whatever they want and they can also
delete whatever they want. So you may not get complete information from a template such
as this, but it does remind the clinician about particular types of information that
you might want to collect regarding an infant who is breastfeeding. And with some effort,
one can actually get this information back out of the electronic health record, even
though it is actually stored as text. Here's another example of a template that is used
as part of a healthy weight assessment that has a little bit more structure to it and
also a little bit more detail. In this case, the clinicians still have flexibility
to ignore certain prompts. They don't have to respond to all of these, but it serves
the double purpose of reminding clinicians about the topics that are important to discuss,
as well as providing some structured response options as shown in the yes/no response options
in the highlighted in yellow. So it gives you a lot of sort of yes/no indicators for
different categories of information and can be very useful and easy to extract information
from. This slide shows examples of documentation from three different providers all who cared
for the same patient at different points in time. The first example, which happens to
be a note that I wrote, was at the newborn visit where I attempted to the best of my
ability to quantify the formula intake for this particular child. The next note was somewhat
later at a follow-up visit by one of my colleagues. The final note is actually from a specialist
who we had consulted regarding this child's poor growth status, who again attempted to
quantify information. As you can see, we're all generally collecting
the same kinds of information, including the caloric strength of the formula that's being
consumed in the case of the specialist. Although it may not have the degree of precision that
might be desired for research purposes. Here is an example of some narrative documentation
related to the nutritional status of an adolescent patient. As you can see in this note, the
clinician captured a 24-hour diet recall with some attempts at quantifying specifics about
the oral intake, such as the number of pieces of pizza and the number of chicken wings that
were consumed. At the bottom of the screen as the assessment and plan for this same child.
As you can see, the clinician has recorded an assessment of the malnutrition status with
some quantified information, as well as some thoughts about the potential causes in the
context of this child's medical complexity. On this screen, we see an example of self-reported
nutrition status that is collected as part of a pre visit questionnaire.
I think this is a promising mechanism for improving the collection of quantifiable data
related to nutrition status going forward. This is currently in a pilot project phase
in our organization, but has been found to be very acceptable to our patient families
and is also very much appreciated by the clinicians who feel that these types of information are
useful to collect. Finally, I would like to share some concluding thoughts. I think there
are definitely many opportunities for improving the current status quo of how nutritional
information is collected as part of routine care, which could be very helpful for observational
research studies related to nutritional status. And clinical teams really are willing to collect
information about nutrition status, but they need to know how to do so, for example, by
providing them with templates or guidance on what questions to ask, which need to be
incorporated into clinical workflows. And it's also necessary that clinicians feel that
the information is being collected is actually important.
And from the clinician perspective, that means how might this information be used to improve
health outcomes in some fashion? I hope this whirlwind tour of a few aspects of how nutrition
is assessed in routine primary care has been somewhat thought provoking, and I look forward
to our discussion on how we might improve the documentation of nutritional status in
the future.
LEAH MCKEEVER: Hi, I'm Leah McKeever. And I am the methodologist and director for the
ASPEN Clinical Guidelines. I'm gonna talk to you today about what I think it would take
to create a good pediatric malnutrition guideline, though everything I'm about to say applies
equally to adult malnutrition, and I have no conflicts of interest to disclose. Now,
to help you understand the issues in this field, I need to first expose some very pervasive
myths in our field. People seem to think that all study designs can answer all questions
with the exception that some study designs are more rigorous than others. The truth is
actually far more complex than that. The reality is that all study designs have assumptions
that must be met in order for them to be informative at all. And many of the research questions
in nutrition violate these assumptions in a way that renders these study estimates either
completely invalid or misinformative. For this reason, I'm very careful how I set up
study design inclusion criteria on a guideline, we usually start by looking for randomized
control trials, because these studies will provide unconfounded estimates.
When RCTs are not available, we will look for quasi-experimental designs. Now, we may
drop down into observational prospective cohorts, but only if there are no known unmitigated
confounders for a research question. And we're gonna describe that in a second 'cause, you
know, usually there are unmitigated confounders. So this system right here limits us to only
using studies where the data was collected for research purposes, and that utilize a
study design that can actually answer the question at hand. So what is the problem here?
Why is confounding such an issue? What's at risk? Well, in all studies, we're looking
at the relationship between two variables. In this case, let's look at the relationship
between nutrition and clinical outcome. We know that there are people who are more severely
ill and that more severely ill people will have worse clinical outcomes. If that disease
acuity is also associated with nutrition exposure, then an observational estimate is not going
to be valid. In this case, the estimate that you get for
the nutrition outcome relationship will be confounded. Another word for that is actually
blended with the relationship between disease acuity and outcome, and we won't be able to
separate them, OK? So the relationship between nutrition and outcome will be blended in the
estimate with the relationship between disease acuity and outcome, and therefore you will
have an invalid estimate. That resulting estimate is not just an issue of it lacking in rigor,
it is literally misinformative. This is the problem for us because disease acuity is inseparable
from malnutrition status. There's a reason our patients are malnourished. An RCT or quasi-experimental
study bypasses this issue. On the left, you see an observational study and you can see
that almost every aspect of a patient's care relates to their disease acuity, including
nutrition. So there's tons of confounding here. In an RCT, which is what we see on the
right, all those variables still connect with outcome, but none of them connect with nutrition
exposure because nutrition exposure is randomly allocated.
So we now have a clear signal accounting for the relationship of interest. I'm sure there
are many that will say, yes, but we adjust for illness severity through things like,
you know, for adults we use Apache II Score, we have illness severity indices. The problem
is that illness severity is not a hard endpoint that you can cleanly capture. These indices
are a constellation of surrogate exposures that just predict mortality. Compare that
to adjusting for age or sex, right? Two hard endpoints that can completely mitigate the
confounding through adjustment. With illness severity indices, you may dampen some of
the confounding but it's still going to leave plenty of residual confounding to directionally
push on that estimate.
DR. LIAM MCKEEVER: In these cases, what this means is that the observational research simply cannot
answer the question. It's estimates are not just uninformative, they are actually misinformative,
if there is any known unmitigated confounding. So what can we do? Well, I think your field
has two overarching questions that need to be answered. One concerns construct validity
of your tools. We need to know that your tools are measuring what we say they're measuring.
Second, we need to know whether intervening on the results of those measurements leads
to better clinical outcomes. So let's tackle the first one. So we need to make sure that
our tools are properly validated. This can even be done retrospectively against a gold
standard measure of body composition. So the current gold standard is Radiographic Imaging.
And at this point someone usually says, but if you do that, you're gonna be limiting your
population to cancer patients because they're the only ones with Radiographic Imaging. I
don't think that's actually true. We can do an abdominal pelvic CT scan for
any undiagnosed abdominal pain, but let's just go ahead and say that's true. Fine, do
that. Show us that your tool is properly validated in cancer patients. If it validates well,
and it also validates well with other less invasive tools, then the use of those less
invasive tools in other populations will become more meaningful because we've seen the tool
properly validated against a gold standard in cancer patients. Another common comment
that I get at this point is to say that malnutrition is more than just body composition. And I
think usually they're talking about the fact that we're looking at risk. My question for
you is, is it really? I mean, you're a container of carbohydrates, proteins, fats, vitamins
and minerals. From a research perspective, the most stable of these is protein status.
So that's our gold standard for body comp. And if you're malnourished, that should be
detectable as a loss in protein status. If you're instead at risk for future malnutrition,
that should be detectable in the future as a loss in protein status.
So we do need to properly validate our tools wherever we're able. We then need unconfounded
study designs to tell us that intervening upon malnutrition findings alters outcomes.
In many cases, this can be done through a randomized controlled trial, and the common
objection at this point is, yeah, but that's not ethical. We cannot randomize someone to
become malnourished. Well, you don't have to. Instead, what you'll do is you'll randomize
to nutrition exposure and then you can look at how the nutrition outcome relationship
differs between malnourished and normal nourished patients. In epidemiology, we call that effect
modification, and in statistics, it shows up as an interaction term in your model. In
populations where standard care fall short, we need RCTs. So if standard care is falling
short of estimated needs, then we need RCTs that compare an intensive nutrition model
to standard care. This can come about through enteral feeding algorithms or supplemental
PN. No one is getting randomized to malnourishment here, so the IRB should be happy.
In your study, you will have both malnourished and normal nourished patients and you're then
going to see if malnutrition status modifies the effect of nutrition on clinical outcome.
Again, this is a simple interaction term in your model that allows you to see if malnourished
patients respond differently to nutrition than normal nourished patients. Now, in some
populations, we may have put the cart before the horse unfortunately, and started intervening
with intensive nutrition without the necessary supporting data. In this case, you may not
be able to ethically withhold nutrition. So in this case we'll have to use quasi-experimental
study designs. So what you'll do is you'll go back in the medical records to the moment
where you began intervening, and you will compare mortality data from before that intervention
to mortality data after you began intervening. Now you'll have to account for patient differences
between those two time points. So you're gonna obtain a robust collection of ancillary variables.
This may be a good place to use propensity score adjustment, but this would be a way
to address this problem if intervention is not an option. So this is what it's gonna
take to demonstrate efficacy. Most PICO questions on a clinical guideline would likely not be
able to include the vast field of available observational research that we currently have
because again, its measurement is flawed and its estimates are gonna be misleading. This
is not better than expert opinion. I know we think that, but it isn't. Observational
research, if it's confounded, is not better than nothing. It is actually misleading. So
it's worse than nothing. And so in this case, we would forego that and instead do expert
opinion. This is what we would need for most PICO questions on a malnutrition guideline
until we get the proper research. Now this is not a major problem, we can do a guideline
that's all expert opinion, but we can only do so many guidelines at once. And an expert
opinion guideline is not always a major priority compared to guidelines that have actual research
behind them. So until there is proper research in our field
of malnutrition, we may be better served by position papers. But I very much look forward
to discussing this further in our panels, and I thank you for listening. Thank you for
these fantastic, very stimulating discussion presentations. I'm excited to welcome you
all back to the Q&A session, and I'm also excited to welcome our panelists. If participants
want to still submit questions, please feel free to do so. You'll be able to use the box
and I will try to get to them even during the panel discussion. So thank you again for
all those who have put in questions. Let's get started. We have questions. Let's start
with Dr. Grundmeier. What a fantastic overview of a topic which is both exciting but also
pain point for many people on the front lines. You've outlined... This question is for Dr.
Grundmeier. You outline many great features within the EMR that can be used to explore
nutritional status. Have you had experience with using this information to create alerts
for patients who might be at risk for malnutrition or to trigger a nutrition intervention or
evaluation?
DR. GRUNDMEIER: Yeah. Thank you. That's actually a great question. Hopefully I'm audible or
somebody will let me know if I'm not. So the short answer to that is no, I have not yet
been involved in a project to do any kind of alerting related to nutritional status
beyond what you would consider to be the creation of passive content where you work through
a set of questions. And then there's maybe some reminders in text that a clinician can
interpret it to decide, for example, whether a referral to our nutritional experts or to
our healthy weight program would be indicated based on the information. But it's not an
automated assessment. It's up to the doctor to think about it. I'm actually now working
with a collaborator who's just starting a project who's very interested in nutritional
status as evidence of child maltreatment, as a specific sub domain. And as part of that,
we are actually going to be making extensive use of the growth patterns. And I anticipate
that those rules we might craft to provide some automated alerting will be useful for
nutritional assessment in general. I think it's a tremendous area of opportunity,
especially when we're asking people to enter a lot of information. You really want to give
them some value back and that is, I think, been a missed opportunity thus far.
DR. NILESH MEHTA: Thank you. That's very exciting. We have a lot more questions related to EHR.
Let me jump to Dr. McKeever. This is a question from Andrea Garber from University of California,
San Francisco. Excellent talk. What are your thoughts on balancing the time and cost of
an RCT with the allure of doing retrospective studies using data that can be drawn easily
out of the EHR? Liam, you're muted. I can't hear you.
DR. MCKEEVER: Sorry. That was a great question. RCTs, yes, they're very difficult. I run them
myself. They make your whole life a nightmare till they're done. But at the end of the day,
it just comes down to you. If you want an answer to your question, you have to ask the
question in a way that it can be answered, right? You need a study design that can answer
it. So for an electronic health record, there are many questions that could probably be
answered in an observational way, and it would just be a matter of maybe there are confounders
we aren't considering. I'm not saying that observational is bad, OK? I'm saying that
if you know there's a known confounder, if you know it and you know that you can't fully
mitigate that confounding, and because you're a scientist, you are starting on the assumption
of the null hypothesis being true. Then you cannot take that estimate and draw causal
inference from it. You just can't. No one wants to hear it, I get it. I never hear anyone
else say it. But it's a mathematical reality. So this is where you would have to do either
a randomized controlled trial or, again, you could do a quasi-experimental design. And
there are sometimes ways at going back into retrospective studies and making the quasi-experimental
study out of that. The whole idea is you want the exposure to be not a product of any aspect
of the person's care. So some people do that by saying, OK, well, we know that this physician
always tends to dose more and this physician tends to dose less. So we'll compare his patients
and that is a way to kind of remove some of that confounding influence. You have to get
creative when you go quasi-experimental, and it's not as good as in RCT, but it does get
around that fundamental issue that says it doesn't really matter what your finding is,
because you're finding it's just measuring illness severity.
DR. NILESH MEHTA: Well said. Yeah, we'll have more discussion around this when our panelists
join us in terms of study design. But well said. Dr. Grundmeier, question from Vijay
Srinivasan from University of Pennsylvania, not too far from you. Can we leverage informatics
to develop pipelines to automatically extract nutrition data from EMR and to provide information
on adequacy, especially around interventions in real time?
DR. GRUNDMEIER: Well, yes, we can with sufficient resources build, really improve on the current
state. I think one of the barriers right now, which I alluded to in my remarks, is so much
of the richness of what at least in my settings we're trying to capture currently end up in
free text documentation, which is always a little challenging to build pipelines around.
But of course it is possible. It just takes some work. And there's, I think, an unanswered
question of, is the work better spent trying to build the pipeline to deal with the data
you have versus redesigning the way you're collecting the data in a way that is relevant
for clinical practice, gets the research data quality data you want. And one of the things
I think is very promising is patients are really willing to collect data for us potentially
in ways that are more valid than one pediatrician versus another, asking the questions that
they learned to ask 20 years ago and probably don't ask the same every time. So certainly
good opportunity to build pipelines. Real time is always a little bit of a challenge.
I mean, soon enough is probably quite possible in terms of, like for example, involved in
registries where we put, well, nutrition information is in it, but not to a high degree of granularity.
But it's pulled out every month and cleaned and there's a review process to make sure
it's OK. And that's often good enough for research purposes if you can get back the
past the issues of bias and confounding of course. But real time is a little more challenging.
If you want to do an alert based on something that was just entered in the chart, that is
also possible, but that is a higher barrier and people do that. But it is much more costly
to do that level of real time. I hope that was answering the question. I might have used
that to talk about what I wanted to talk about rather than your question.
DR. NILESH MEHTA: No, that's great. No, thank you for that. This is great. I think there are more
questions coming in, but this would be a great time to welcome our panelists, Wendy Sue Swanson,
Cernich, Julie Patterson. And well, you guys are coming on board. A very quick question.
Back to Dr. McKeever. This is from David Suarez. Is validation against a gold standard? Validation
of the gold standard doesn't actually measure the outcome in question. For example, nutrition
screening tools predict mortality but not response to nourishment.
DR MCKEEVER: So that's a problem in my eyes. So if this assumption, we make an assumption
when we use a tool like that. And that assumption is that intervening with nutrition will...
Intervening upon the finding of that screening with nutrition will alter outcomes in some
way. I've seen that with the NUTRIC Score, for example. There's all these papers out
there that show that NUTRIC Score is related to outcome. And from that they say, wow, so
we really need to provide better nutrition based on this NUTRIC Score. But again, if
the NUTRIC Score is only measuring illness severity, of course it's related to outcome.
There's not a single nutritional parameter on the score. So to properly look at the NUTRIC
Score, you would have to have a randomized controlled trial that looked at whether intervening
upon that score with nutrition impacted clinical outcomes. And without that, I don't see its
utility.
DR. NILESH MEHTA: Thanks, Liam. With our panelists on board, this is a question for all three
of you. And we can go around the panel to ask. In this setting of malnutrition gaps
and research identification which is our task, can you discuss some of your study designs,
particularly those that could be applied to explore pediatric malnutrition from the lens
of prevention, identification or diagnosis. Essentially something in your repertoire or
past or current experience that we might learn from? We can go around the panel for this
one. Just jump in. Thank you so much for that question. Oh. That's OK. Go ahead. So my research
actually emerged as a result of the Joint Commission identifying a standardised definition
of exclusive breastfeeding. And I think what we've heard in this conference is the need
for standardized definitions. And so an entity such as the Joint Commission working in collaboration
with them to develop these definitions and then track them, allows you the opportunity
to look at outcomes across health care systems using a nationwide dataset.
So that's exactly what my research kind of centered on, was looking at exclusive breastfeeding
outcomes using the Joint Commission definition in baby-friendly and non-baby-friendly hospitals
while looking like controlling for demographic characteristics as well, which I obtained
from the American Communities Survey. So I feel like, you know, sometimes it requires
merging several data sets in order to answer those research questions. How easily accessible
were these data sets that you relied on or that you accessed? I think that it's good
to have friends in good spaces, and that would be some of my collaborators at the University
of Wisconsin-Madison as an example. The University of Wisconsin-Madison developed the neighborhood
atlas, which was another indicator that I added to my most recent study, which was exploring
the area deprivation index. And again, in this conference, we've learned a lot about
the relationship between where a person lives and the need to provide context around where
a person lives. And so the area deprivation index is that
tool. All you need is the person's address, and then it provides you with a number that
gives you a level of deprivation. So I applied this to my most recent dataset, and then I
looked for access to baby-friendly hospitals in areas that were highly deprived versus
areas that were low deprivation and then looked at exclusive breastfeeding rates across those
area deprivation indices. So I feel like as we talk about risk factors, understanding
where a person lives and what they have access to, the area deprivation index is a readily
available tool. It includes 17 variables from the American Community Survey. It collapses
those into one very great number that you can use to interpret the resources in a person's
community. And it can be a great screening tool, I believe, as well as a research tool
to look at outcomes. Thank you so much. That's very helpful. Let's move along the panel.
Who wants to go next? Oh, I'm fine with going next. So I have been involved with POWER,
the Pediatric Obesity Weight Evaluation Registry for the past eight years.
And this has been a...the intent was to have national participation of comprehensive multicomponent
pediatric weight management programs. And to get people to sign on, this was something
that comes as a cost. And so we do at this point, over the eight years we've been doing
this, we have not been able to get outside funding to support programs, to either systematically
be collecting data, to, you know, have the resources on board. In which to do that, we
have options for both manual data entry as well as electronic upload that requires IT
specialists at the sites to help do that. We also tried very hard to standardize the data
collection, make sure we're getting data elements in some kind of systematic way. So when I
listened to research design, when I'm just trying to put the infrastructure together
to get at outcomes, which is programs that are set up in a certain way, who's doing better
based on either the resources they have available, the staffing, the training, there are so many
outstanding questions. And the research that we publish so far of
the data elements that we have, have yet to point to something either in program design
or even patient characteristics that say these do better given this very generic way of providing
multicomponent pediatric weight management services. So even though eight years we've
spent on this, we're sort of still in what I would refer to as the infancy and trying
to discern this, we've yet to establish a consensus on an outcome that we see as successful.
What measures should we be pointing to that says with pediatric obesity, you have a successful
outcome? We had a previous presenter that says in the adult world, we have markers of
know when you have success. But with pediatrics, that doesn't exist. We've yet to have a consensus
on either the outcome measure or the outcome itself to know we are doing better. (LAUGHS)
In terms of the next step that would be, if you had a magic wand, where would you want
to see help? I know you mentioned external funding, of course, for something on a large
basis like this. Anything else that you would prioritize as
a hurdle that might help you jump to the next level? I think if sites had access to be able
to do the electronic upload of the data of interest, so you remove that as a barrier.
If we use more validated screening tools and have the electronic medical record, have that
in place, or whether it is established templates that people who are participating in this
are using that reliably. But this all requires from an infrastructure standpoint, rather
than just designing the study, we need to expand. We were at a point where we have 42
sites participating at one point in time. They can't sustain the participation. We have
our own data coordinating center at Cincinnati Children's that comes as a cost. But we use
what's called the rave data system that's very high quality, and does the sort of thing
that you heard the first speaker talk about making sure that we remove data that is not
reliable, that is outside limits. So we're able to do quality checks.
But I still think that we can do far better and getting a representative registry that
doesn't have the barrier of cost to participate. Thank you. Let's go to Wendy Sue Swanson.
Your comments on the same question. I'm sorry. I can't hear.
WENDY SUE SWANSON: Oh, I'm sorry. Did you call me? Sorry, yeah. (CROSSTALK) Yeah, sorry.
I'm Dr. Wendy Sue Swanson.
DR. NILESH MEHTA: Do jump in. Yeah.
WENDY SUE SWANSON: I was, you know, I think I have a very different take. So I was at
Seattle Children's Hospital for about 10 years doing digital health and innovation. And now,
in part, because of funding challenges, I'm now both at Stanford in digital health. And
also with SpoonfulOne, which is an outside-funded venture capital-funded company working on
diet diversity in early life, early allergen and exposure. But one thing I wanted to just
bring up is, you know, I'm sitting very intentionally in the kitchen, in that, you know, so many
of these challenges for families, we don't solve at clinic, and we don't solve them in the
hospital. They're solved ultimately in the kitchen. And, you know, I started my work
in digital health as a mom blogger for 10 years really translating health and science
information, because I wanted families to reach that. So when we're thinking about clinical
trial design, of course, the more convenient we can create in any design, and the more
digital in my mind, we can scale. So one thing you can look up, but the INTENT Study, so
it's I-N-T-E-N-T study.com, you'll see a lot of this.
But we designed a trial where we were able to recruit 1,700 infants between the age of
four and six months in less than 12 months. And we did that through multiple different
channels through experimentation. It's a full end-to-end digital trial, meaning that families
never go to clinic for outcomes, we rely on patient-reported outcomes for primary and
secondary endpoints. And we also, you know, have a pretty long. It's a randomized, controlled
prospective trial design, it'll run 18 months. So it's pretty long. Families, though, however,
could learn about it online and social channels. We also tested through Duke Clinical Research
Institute, entire, you know, swaths of any child who aged in at four months of age through
my chart, getting an invitation to learn about the trial. And then we use, you know, Facebook,
Instagram, traditional social media channels. And we also use BabyCenter. And what's so
extraordinary about BabyCenter? Is it's where moms and dads already are. So when I would
go to recruit a group of four-month-old infants, I could go to 165,000 moms to four-month-olds
right then, describe the trial design and, you know, let them learn about their eligibility.
They fill out eligibility online. When they met eligibility, they download an app. After
filling out informed consent, they're immediately randomized into either the intervention arm
or the control. And then they began the trial through the app that has internal and external
intrinsic, you know, validation and gamification, and also allows families to input information
there. So, you know, I think the goal here in some ways of joining this panel is to think
really critically about populations. We got children from all 50 states. 30% of our study
population had by the [inaudible], inventory, a diagnosis of atopic dermatitis, and we're able to capture,
you know, 1,700 infants that we can now follow in that time. And this was through the pandemic,
that we were able to recruit them. And so my hope in some ways in joining this is just
to, you know, we did a number of different trials at Seattle Children's that involved
user-centered design, family-centered care methods that bring patients families and caregivers
to the table in the design of how you actually create technology, how you create the study
design and how you put it in so that, you know, that convenient model is really a part
of how we get there. And I think, you know, what was interesting
about this trial and our recruitment, we fully recruited back in April. So we're in the midst
of data collection at this time. And like, you know, some of the presenters have talked
about, that's kind of what, you know, what I live and breathe at this point until we're
done with the trial in about a year. But, you know, when you have an infancy, of course,
during these critical periods of development, and these critical periods of iron intake
and all the other issues and critical periods of early breastfeeding, for example, there's
such small windows of eligibility and enrollment. That to scale and to get the numbers that
we often need, in this case, we're looking at outcomes for food allergy diagnoses, which
because of the low rates of prevalence in early life, we required large trial designs
just to really think critically about getting these studies into Pearson's hands through
mobile apps, really thinking critically about where to moms and dads live online.
And how do we go find them, educate them, you know, garner their informed consent, and
involve them in research moving forward, using social tactics like videos and education,
nudges, notifications, text messages, etc. A part of the way they live their entire day,
bring research into that same model.
DR. NILESH MEHTA: That is really very exciting. And congratulations on all your success. We discussed
this briefly in our previous session. One of the discussion points we had, Dr. Srinivasan
from Pennsylvania alluded to that is the concept that using social media platforms or digital
tools for data gathering in studies may limit the study to only populations that have access
to it. And there are plenty of examples that people don't have access to an app or computer
or a phone. Any comments on that? How do you overcome that?
DR. WENDY SUE SWANSON: Yeah, thanks for your question. I think over the last 10, 15 years that I've
been in digital health, it's always the first question because we worry about marginalized
populations. You know, the act that was just signed-in in this last year on broadband Internet
access, 30 more million Americans that Biden signed in, for example, are collapsing the
gaps that people have towards broadband access. And in reality, if you start to look at the
Pew Internet data, that we ultimately know, increasingly, you know, north of even 80%
of 12-year-olds have access to a smartphone, and increasingly, that it's not a reason not
to get involved using these digital tools and tactics. But to your specific question
about social media, I want to bring that up. Data collection in social media is fraught,
you know, without question. You know, when we did recruitment, for example, using Facebook
and using Facebook or meta now owned Instagram, we actually had a lot of challenges with hackers
from all over the world. But we had to rechange our patient identification
and verification process at the beginning using IP addresses from exactly where people
are. The goal in some ways I would suggest is to, from a recruitment standpoint and uptake,
is to find people where they live, where they think, where they learn, and where they share,
and bring them into secure environment. So in partnership with Duke Clinical Research
Institute, we used another app called Pattern Health that had been used in multiple other
clinical research trials, never fully intended virtual. But we use it after it had been verified,
it had been encrypted, it followed all the recommend, and then brought patients and families
into that environment for data access and for data gathering in that way. Because I think,
you know, I'm increasingly, I mean, I was one of the very first physicians on Twitter
and Facebook, one of the first bloggers in this country for a pediatric hospital. But
I think that I'm increasingly skeptical and concerned about the kind of ownership of these
social channels and their lack of care and concern for both privacy, human rights, public
health and education. So I think we go and get people and we bring
them into a place where we can really protect them and serve them in ways that we would like.
DR. NILESH MEHTA: That's a great point. Dr. Grundmeier, Julie Patterson had this question too, relate
it to some other presentations in this workshop, particularly tools such as photographs, the
mobile food record, and I know other companies that have invested in it. Do you see opportunities
for these type of apps on patient or caregiver's phone that can be integrated into the EMR?
We've certainly seen photographs being included for other purposes in the EMR.
DR. ROBERT GRUNDMEIER: Yeah. Question for me? That's great. (CROSSTALK) One of my favorite topics
right now. And it's actually related to the notion that families will provide us more
accurate information than we're able to collect in the office in our limited time. And I think
the variety of apps that are available for collecting data sets such as the example of
taking photographs to quantify nutritional intake, which is, I think, really exciting
in terms of possibility of making it, providing new ways for families to easily capture information.
I mean, the number of times I've asked a family to just keep track of nutritional intake for
three days or something like that. And it seems to be a big barrier for people to do
that the old-fashioned way. Whereas taking pictures is great. The frustration I have
is that there are always people making these wonderful apps. And it seems like almost nobody
is thinking about how that information could be incorporated into routine clinical care.
And that, I think, is an incredible gap and opportunity that I really hope future
research efforts would address. I could go on and on, but it has to be done with great
care because clinicians don't want to be deluged with information that they don't know what
to do with. But with thoughtful approaches, these new types of data capture could both
be useful for research, patient engagement, and self-management, as well as helping to
inform the clinical team so that they can do their part to navigate the family towards better outcomes.
DR. NILESH MEHTA: Thank you. Thank you so much. Yeah, it's an area that many people are watching.
There is, of course, patient confidentiality in addition to extracting these in an objective
manner to provide information. A question for the panelists: at this point, yesterday,
around this time, we talked about disease-related malnutrition. We alluded to a specific group
of diseases. And we found that many of these researchers found that these unique situations
as a result of being pigeonholed into their unique areas in order to explore the possibility
of pushing the field towards multiple disease condition associated malnutrition areas at
the same time, do you have any opportunities that you see how you can combine some of these
disparate conditions? And I don't know if that means registries
that house many or trials that incorporate distinct disease-specific malnutrition. And this is a question for anyone on the panel.
DR. SHELLEY KIRK: I'd like to address that if I can. I don't know if I'm still on mute or you can hear me.
DR. NILESH MEHTA: No, you're good. We can hear you.
DR. SHELLEY KIRK: OK great. One of the things we captured in our registry is that for each patient encounter,
we also list medical diagnoses. So it's not just about obesity. So we're capturing whether
it's dyslipidemia or we're capturing NASH or any of the other comorbidities of obesity.
We have an opportunity to look at that. And so what we've recently added to our registry
are liver health measures. And this may be the first time that will be able, in combination
with obesity, be looking at NASH and NAFLD and see of the additional measures that it's
not just about change in liver enzymes. There's a lot more going on to investigate. So this
may actually offer the first time having a registry that's looking at liver health and obesity combined.
DR. NILESH MEHTA: Thank you. Anyone else? Any other thoughts?
A question for Dr. McKeever, in addition to the cost of doing an RCT, many RCTs in recent
times because of the nature of an RCT and the need to conform to the experimental condition
find themselves lacking in external validity. So, for example, many RCTs have had patient
eligibility criteria such that a very small fraction of my practice actually caters to
their trial eligibility. And therefore, when I go back to my practice, I barely find external
validity. Any comments on that, Liam in terms of our RCTs?
DR. LIAM MCKEEVER: It's a...it's a really important consideration, right? The consideration of who does this
generalize to is the most important question you can ask anytime you read a study. So here's
the thing. It depends on your population, right? So like I did research in adult septic patients,
that's an incredibly heterogeneous population. If I were going to just do that pragmatically
or just, you know, a "come all", then you really need a fairly large sample size to be able
to deal with that heterogeneity. And a lot of RCTs are not in a position to do that because
they're expensive, they're time-consuming. And so I think the kind of... I think the
line of questioning has to go like this, in my opinion. I think, you have to first say, can we
demonstrate the efficacy of this in any population? Clean it up. And if you can demonstrate, yes,
with people with acute pancreatitis, this is happening, this intervention is working.
It might not be working for these people over here, but ask yourself what would actually
be the value of a population estimate anyways, right?
This idea that everybody needs the same thing is not true. One thing that was brought up
before we started this and I'm so sorry I can't see the names of all the panelists and
I don't know which one brought it up, but she was talking about like, maybe we should
be collecting data on genetics. And wouldn't that be great if we could do that? And there
are people who do that like the (UNKNOWN) collects data on septic patients, actually
has a gene chip, and she's got a huge database. This, you know, we need to start thinking
about like what people is this intervention going to work for? And if we keep sticking,
you know, I run the guidelines, but I'm the first to say a guideline is a population estimate.
I'm not sure how useful that is. It might be good for some and bad for others. And I
think the null findings we often see is that some people are benefiting from it and some
people are not. And it's all equaling out in the middle. So personally, I don't have
a problem with this idea that you need to get specific.
We just need that many different population studies happening. We need robust funding
and robust studies. Otherwise, I'm not sure that our pragmatic answers are going to give
us anything more than a population estimate, and I don't know how useful that is. Yeah.
DR. SHELLEY KIRK: I'd just like to add that POWER, our registry has just added genetic testing as
an additional data element going forward. And the reason why we're looking at in-depth
liver health or genetics is that we're not getting the answers we're looking for in terms
of figuring out who's going to be successful in our interventions or not, because we're
not collecting the information that might uncover what it is that we're missing. That
is the advantage of the registry that has established that once you hit a roadblock,
you can change that. You can do it differently. So I appreciate you bringing that up because
we're poised to get that kind of data going forward.
DR. LIAM MCKEEVER: I'll tell you, my research showed in a very small sample and it has to be reproduced,
but it showed that 40% of the adult ICU population has a genetic snip and the genetic allele
status such that those people will create more oxidative stress from nutrition versus
people who don't. So it's like for those people they might need a completely different intervention,
a completely different dosing, and you can't capture that any other way. Right. I think
you're on the cutting edge if you're doing that. That's great.
DR. SHELLEY KIRK: Thanks. DR. NILESH MEHTA: These are great comments alluding to what both of you said. If you
have identified a relevant patient population, whether it's the adult with sepsis or children
of a certain age who are going to undergo some kind of nutritional intervention, like
the POWER registry or others that we've heard of. One thing that's growing in some areas
is the concept of platform trials, wherein you then move from one intervention to another
but utilize the resources that you've invested into developing the ability to gather data,
whether you're using EMR or whether you're using research personnel. Liam, first you and
then anyone else after that, jump in platform trials gathering strength. Are you seeing
any emerging?
DR. LIAM MCKEEVER: I'm not aware of anything. I'm not 100% sure, though, that I followed the whole question.
DR. NILESH MEHTA: Yeah, it essentially is a population of interest and then the intervention changes. So you
finish one succinct trial and then you can actually go for the next intervention in that
patient population. So all your resources that you've invested in developing the infrastructure
continue to be used and the enrolment and everything. So you are...it's a force multiplier.
And the adult critical care group, the Canadian Trials Group, have good examples of that.
And I know there are efforts in certain parts of NIH as well that they've seen proposals
like that.
DR. LIAM MCKEEVER: I think it's a great idea. It's a wonderful idea. Go ahead.
DR. SHELLEY KIRK: One thing that we also have established in power are these ongoing reports and what we're able to report on selected
outcomes that we're looking to expand on those reports is having six-month cohorts. So if
a program wanted to try out something new, they can be looking at their outcome data
for a particular six-month period and compare that to a previous stretch of time. And again
it would be over six months and only include those that were enrolled during those different
cohort periods. So we have an infrastructure here to answer quite a bit. We just need more
resources in which to make sure we have a representative national population that has
the diversity. That's the other thing that a registry is going to be able to do is capture
populations both from a geographic standpoint as well as a race-ethnicity. And I think then
our outcomes are going to be more applicable when we can't account for those differences
in the patient demographics. DR. NILESH MEHTA: Thank you. Yeah, that's a great point. Dr. Grundmeier,
we're talking about resources and the ability to harness what's available. EMR,
of course, will remain in the spotlight, and frankly, a lot more to be desired. Where does
natural language processing and the ability to use it and then gather information that
can provide both clinical decision support, but also research data?
DR. ROBERT GRUNDMEIER: Yeah. Actually, I was going to make a comment about one of the things I've been thinking about is listening
to the conversation is whether the EHR can be a part of the mechanism by which randomized
clinical trials could be completed more efficiently and in more diverse, you know, a broader variety
of populations. To your question about NLP, things are getting much better. There's a
lot more tools and ways to develop NLP pipelines. I am less aware of whether it's been extensively
applied to the nutrition challenge, but I think we've all had the experience of NLP
helping us in our everyday lives in different ways, whether it's Siri or Alexa.
So it's really close. I can remember when I started my career 20 years ago, we were
just waiting for automated voice recognition to be a thing we could do and it was really
close for a long time. And now it's, you know, now it works. So I think we're right at that
cusp with NLP where increasingly you can get actually accurate, reliable information through
pipelines that are more or less off the shelf. We're not quite there yet, though. It still
takes a lot of tinkering and a little bit of research and development efforts. So but
I think that's going to be and that's what I say now is I don't worry so much about enforcing
a structure on the data collection process, but just try to get people to give you the
information that you need in some fashion in the text, and then we can get it back out
afterwards because that tends to be more acceptable to people than being forced to check boxes.
Doctors don't like to do that. That's not the way we're trained. So yeah, I think that's
the way going forward will be to allow free text information to be collected and develop
better ways to make efficient use of it. DR. NILESH MEHTA: Thank you. Thank you. We have time for one
quick comment from Julie Patterson. Julie, please jump in.
DR. JULIE PATTERSON: Yes. So thank you so much. I just wanted to kind of share one of the
things that I'm hearing people say is sometimes barriers and facilitators to implementing
change. And so I want to kind of put in a plug for dissemination, implementation, science
frameworks. There's a really great resource dissemination-implementation.org, which has
a kind of a tool that you can use. A lot of times it's hard to identify which dissemination/implementation
framework you would want to use, and it helps you to identify what stage of the process
you're in. Are you looking at developing an intervention or implementing evidence-based
practices so that there's a really good opportunity for implementation science to be a strategy
for overcoming a lot of the barriers that we've heard of in this conference in terms
of identifying and diagnosing patients with malnutrition. And then finally, just to put
in a plug for understanding where your organization is at. There's a really great organization
change management tool that can assess kind of the readiness for change and the readiness
to develop these or to implement these interventions. And I think that we see better success when
we have all parties engaged. And so there's a great tool that I can put in the resources
information that will be available, I believe, after the conference, along with the links
for these implementation science frameworks to help people think about research, study
designs, and strategies.
DR. NILESH MEHTA: That's wonderful. Thank you for doing that. Thank you for your comment. To all our
participants, we are almost at the end of this session. I hope that you will now spend
the next hour until 3:45 and visit the poster hall. There are some fantastic abstracts,
posters displaying work from all around the world. The poster session is now going to
be open for the early life track two. At 3:45 right after the poster session, we come back
and this is the last and a very exciting session on early life Malnutrition conclusions. I
will be joined in moderating that session by my colleague, Dr. Todd Rice, and my colleague,
Dr. David Seres. In addition, we have a very esteemed panel to help us discuss some of
the future avenues for this area. We have Professor Christopher Duggan from Boston Children's.
William Evans from University of California, Berkeley, Liam McKeever joins us back. David
Suskind from Seattle Children's. Tamara Hannon from Indiana University and Vijay Srinivasan
from Children's Hospital of Philadelphia. So I hope you will join us. At this stage,
I just want to express my deepest thanks to our two speakers today, Bob Grundmeier,
and opening up the EHR campaign for us. It will stay on our minds. And Liam McKeever,
always challenging us with the study, design, and research, and we really appreciate that.
And huge thanks to our panelists. Julie Patterson, Shelley Kirk, and Wendy Sue Swanson. Thank
you for your insightful comments. Thank you all for joining us. See you at the poster
session. And then at 3:45 for the concluding session of this workshop. Bye.
DR. NILESH MEHTA: Hello, everyone. Welcome to this most exciting
and sadly, the last session of our five day workshop. This is session 11. This session
is Early Life Malnutrition Conclusions where we bring together lessons learned, dreams
dreamt, expert panelists from previous sessions, and then hopefully with input from our participants,
try and discuss the future of malnutrition research in early life. I'm delighted to be
joined in this session as co-moderators by Dr. Todd Rice from Vanderbilt University Medical
Center, and David Seres from Columbia University Irving Medical Center. Although I will be
speaking to begin with, they will jump in intermittently as well. In addition, we have
a very exciting panel. Our heartfelt thanks to this esteemed panel for their generous
time and for spending the next hour with us. We have Christopher Duggan from Boston Children's
Hospital, William Evans from UC Berkeley, Liam McKeever from the American Society for
Parenteral and Interim Nutrition, David Suskind from Seattle Children's Hospital, Tamara Hannon
from Indiana University School of Medicine, and Vijay Srinivasan from the Children's Hospital
of Philadelphia. Welcome to all of you panelists and my co-moderators.
As I mentioned, this session goes on until as long as we want, but hopefully we'll shut
it down at the end of an hour if we do get there. After that, I will close the session
today and please do hang on until the end because I have the poster awards for the adult
and pediatric posters that will be announced right at the end of this session. So having
said that, I am once again going to thank our NIH Scientific Leadership Christopher
Lynch, Charlotte Pratt and Ashley Vargas. Their foresight into thinking this and creating
this platform for such a lively discussion on five days is tremendously appreciated.
It has been a great opportunity for us to identify gaps and think about future studies.
This particular session is important because we bring together panelists and speakers from
previous session, and what we would appreciate is for the panelists to provide their own
thoughts. They would use their past experience, their expertise and their view of the sessions
that they've had the chance to attend in the last five days.
And based on that, I am going to ask them in the beginning to start diving into what
they think would be a classic, a pivotal moonshot study. What would be the moonshot study that
they would want to see that promises improving outcomes in pediatric malnutrition or prevents
pediatric malnutrition and thereby improve outcomes; which was the basic premise of our
workshop. So their ideas on research opportunities and gaps, how to close some of the gaps that
were identified in the workshop so far would be also very helpful. So I look forward to
a semi-structured but hopefully a, a generous and exciting engaging discussion. So let's
kick this off. Once again for those of you who are participating, if you look at the
box on your screen for questions, we would love for you to send in your questions, your
thoughts, your comments along the lines of anything you heard in the workshop specifically
related to the future studies that you would like to see. At this point, I would let my
co-moderators Todd and David say hello to everybody.
Alright, David waved and Todd waved, thank you, welcome. Let's kick this off. I think
I'm going to start as I promised, asking each of the panelists to spend some time to kick
us off in relation to what they think would be a moonshot study that they would like to
see, a pivotal study, a classic study. And I will start this with, let's start with David
Suskind.
DR. DAVID SUSKIND: Yeah, there are so many studies that I think are needed in the world of nutrition
and pediatrics that would be pivotal, and it's hard to say one, but my focus of interest
is inflammatory bowel disease. And I think as many of us know, this is a condition that
has increased rather rapidly over the last 50 plus years. And nutrition and diet are
integrated into the IBD paradigm because of the microbiome and the effect of diet on the
overall health. And so the moonshot study that I would love to see done would actually
be multiple, but would be looking at how diet impacts disease and disease development, looking
at not only something called exclusive enteral nutrition which we know works amazingly well
for inflammatory bowel disease, not only improving nutritional outcomes but also actually healing
the disease itself. And then looking at that in terms of how a whole foods diet impacts
outcomes. And I think as we all know, there is not enough resources, there are not enough
researchers looking at the impact of nutrition in these types of conditions.
And so really providing those resources so we could do those prospective interventional
nutritional studies in inflammatory bowel disease itself. And that would be my desire
because there are so many different dietary interventions that could impact disease, but
they're just not being done as of yet because the resources are currently not there for us.
DR. NILESH MEHTA: Thanks, David. Very helpful. David, can I ask you what outcome would be your most
priority or what couple of outcomes would you be most interested in in such a study?
DR. DAVID SUSKIND: So definitely look at the nutritional improvement in this population. There have
been two interventions in inflammatory bowel disease that have been shown to improve nutritional
outcomes in this population. One is exclusive of enteral nutrition, the other are fairly
strong immunosuppressants biologics, anti-TNFs specifically. And so we want to look at that,
but we would also wanna look at disease activity. We'd like to look at the microbiome
and the metabolome which we believe are instigators of the disease itself because by looking at
the nutritional intervention, we actually are getting closer to looking at what the
etiopathogenesis of inflammatory bowel disease is and getting our understanding of why this
occurs in patients. We still don't have 100% understanding of why IBD occurs, but we do
know that an intervention that does not use immunosuppressants is this nutritional therapy.
And so we can actually understand the etiopathogenesis by studying exclusive enteral nutrition and
other dietary barriers for this population. So it would be multifocal in terms of the
outcomes we would want to look at, but definitely would want to look at the presumed trigger of
the disease itself.
SPEAKER: So can I just, thank you and ask you to think even bigger than this because
if your nutritional status improves but you don't survive, I don't really care about that.
So beyond these things that are very integral and very important even if just as markers
for severity of illness, what beyond that would you like us to shoot for in terms of
the best outcomes for kids? Cognitive function growth, quality of life, activity levels,
broken bones in their 70s, really think, think bigly here.
DR. DAVID SUSKIND: No, I agree a thousand fold. I mean, we really need to take a holistic
approach to our evaluation and what outcomes that we really want to. From a clinician standpoint,
outside of my actual research, really, I need to look at quality of life, looking at nutritional
parameters, including growth, looking at development, these are all integral for our understanding.
And I think one of the difficult aspects of doing nutritional research is that there are
so many endpoints that we really want to examine and trying to get a platform that would incorporate
all of those. One of the things that I've been working on of late is using digital technology
to really understand the whole world with the whole patient in terms of psychosocial,
in terms of disease activity, in terms of integrating that biomarkers that we look for
in both nutritional outcomes and disease activity and trying to integrate that into platform.
But you're right. I mean, we do need to look at this as a kind of a holistic evaluation
of the patient. So looking at all the parameters that we can.
DR. NILESH MEHTA: Thanks, David. Let's go to Chris Duggan, Chris.
DR. CHRISTOPHER DUGGAN: Thanks Nilesh, thanks the organizers of this really exciting conference.
Is there an echo or is that to me?
SPEAKER: Everybody should maybe mute.
DR. CHRISTOPHER DUGGAN: And whenever I hear the term moonshot, of course, as a Massachusetts
native, I'm reminded of John F. Kennedy's approach, right? And anyone who's flown into
Logan Terminal E will see the quote. And as we all know, he said we're going to go to
the moon, not because it's easy, because it's hard. And indeed when you think about what
a moonshot should be, it should be hard. And in the field of pediatric nutrition, I think
one of the things that makes me think what a moonshot would be would be looking very
critically at what the important populations are at risk of severe nutritional problems
or and as maybe we'll talk about later I view it, I think this has to be thought of globally,
not just in one set of developed economies, but perhaps more broadly. But suffice it to
say we do know that children who have...are subject to inflammatory or infectious stresses
are especially susceptible to malnutrition. And certainly, we know in our country the
burden of prematurity and congenital anomalies, specifically congenital heart disease, those
are two populations that we really have plenty of patients with those conditions that it
shouldn't be hard with enough resources to design a multicenter interventional trial
in which the sample size would be large enough so that significant conclusions can be made
on the interventions. And as if to kind of illustrate the fact that
we're in such a sad state of affairs now, you only have to think about going up into
your local NICU and realizing that ten years ago, we had one form of intravenous fat and
now we have three forms of intravenous fat, and no one can tell us whether one is superior
than the other. They've been brought to market with short term studies, no long term outcomes,
no long term growth outcomes, there are few, I should say, and that's just a great example
of an opportunity lost. So that would be my moonshot. Pick a population of children at
risk of significant malnutrition. And obviously, maybe people here aren't so interested in
the effects of nutrition in other countries but clearly children with pneumonia, diarrhea,
malaria and others are at risk of significant malnutrition globally. And indeed, studies
are ongoing which I'm a part of, one to investigate in interventional, multi-country way what
approach makes the best sense. But here in the United States, it seems to me the burden
of pediatric nutrition has to focus on premature infants and children with anomalies such as
congenital heart disease.
SPEAKER: Just a comment. [Cross talk - Thanks, Chris.] There have been speakers talking about sort of you
know, there is no lack of food insecurity in the U.S. And while this conference was sort
of more about malnutrition in sick people, there is you can't ignore, I think, that continuum
of how they enter into their illness and how well nourished or not nourished they are to
begin with. So, yeah, that's good stuff. Thanks. DR. TODD RICE: Chris, do you think that we know or have some
idea of which patients in those populations are the patients that we need to aggressively
intervene in? And potentially, is the intervention different for different subgroups in those populations?
DR. CHRISTOPHER DUGGAN: Yeah, it could well be. And again, I'm not a neonatologist, but it seems to me that
those children... well, certainly the cardiologists will tell you that the children in single
ventricle physiology would be the ones at highest risk of poor growth in neurodevelopment
over the first years of life. And in the NICU patients, obviously, those children with extreme
prematurity, those children with either congenital anomalies or severe bronchopulmonary dysplasia
would be highly likely to suffer from nutritional problems as well.
But here is where and that question really illustrates an important point. And David's
point about, about IBD illustrates this too, in my mind, which is that if we're designing
nutrition study but don't take into consideration that new biological therapies are going to
be rolled out for IBD and don't take that into consideration or don't think about new
surgical therapies for single ventricle physiology, then we'll be really scuppered. You know,
we really need to develop these and design these studies with people who don't think
of themselves as nutrition scientists, but those who are content experts as well. Yeah, great point.
DR. WILLIAM EVANS: And I'll be talking a little bit about our study in premature infants,
but it might be a good time just... So I said, we published an article called D-3 Creatine
Dilution for the Noninvasive Measurement of Skeletal Muscle Mass and Premature Infants.
We had about 90 infants from a birth weight of 550g to 4.2kg, and we measured them while
they were in the NICU and 20 of our patients weighed less than 1,000g at the start of the
study. So I'll be speaking a lot about that. But
it was my first foray into pediatrics and especially into neonatology. And it actually
it turns out it's pretty easy to measure lean body mass and muscle mass by taking urine
from a diaper. DR. NILESH MEHTA: Thank you, Bill. That's very timely. Bill, in fact, I'll jump to you so
that we can have some continuity right after this. But one of the things that Chris brings
up and related to Todd's question and David's question, a David Seres question before during
the day. When you talk about doing studies in a specific population because you believe
that that's where the burden of the disease and therefore the potential for the intervention
being effective or efficacious or having a signal is more that lends itself to a certain
study design; and you can actually, with a multi-center, get large numbers. But the concept
in recent years has been one of enrichment where whether do you have tools that will
help you identify which ones, even among those are more likely to benefit from the intervention?
And, you know, Todd and David in their world have addressed that with the new trick score
and similar ones. But it could be something as simple as marker of underlying nutritional
status. But any anyone want to comment on how to find that population where you are
more than likely for the intervention to benefit because we now have a graveyard full of negative
randomized controlled trials where we imagine everybody in my hospital or everybody in this
population should benefit from a therapy and they don't. And therefore the summative result
is negative. DR. TODD RICE: You know, I think in the adult world, we're starting to get a little bit
of an understanding of there are patients that are and I think David Seres may have
coined this term, have refractory malnutrition which means that they are truly malnourished,
but they also are malnourished to the point that we can't intervene, at least in their
current state. And there's a really nice article not in critically ill patients which is what
I do, but in hospitalized patients otherwise, that if you have high inflammatory markers
in the adult world, that it actually doesn't improve your outcomes.
If we provide any meet malnourished criteria, if we provide nourishment. But if you have
less inflammatory, high inflammatory markers, then, additional nutritional support may improve
your outcomes. And so the question I think I always have is we, we treat lots of groups
of patients as the same, but there may be patients that the nutritional intervention
has to be part of a different treatment plan, i.e. you need an anti-inflammatory and a nutritional
intervention. Otherwise, the patient's so inflamed that they're just not going to be able
to utilize the nutritional support that we give them.
DR. WILLIAM EVANS: I think that's a really important question. I'm also part of the International Cachexia Society looking at changes in body
composition, particularly muscle mass in ill people and the patient population where you
get the greatest loss of muscle of any patient population are older people in the ICU. And
part of the problem that is not so well recognized is that they all have some degree of enteral
feeding intolerance. And what that is, is actually related to is
unclear. But almost certainly the raging inflammation that they have. So treating with potential,
you know, anti-inflammatory enteral feeding formulas might be appropriate. But as I just
heard, someone said, you know, we don't really know how well they work. They're marketed as
anti inflammatory enteral feeding formulas, but the degree to which they actually affect
inflammation is really unknown. SPEAKER: I would also point to a long littered path of anti-inflammatory
attempts at anti-inflammatory therapies in critically ill patients that have proven either
no effect or be harmful. Some of us are old enough to remember growth hormone in the ICU
or, you know, so that, you know, obviously these would have to be tested in a randomized setting.
DR. WILLIAM EVANS: I think that Chris brought up the area perhaps of maybe the greatest impact
because the nutritional status of neonates in a NICU is literally all over the place.
And what is the best marker of nutrition status and maybe even more importantly, what is the
best indicator of healthy or healthy growth? SPEAKER: I'll come back to this when it's my turn. But
just an answer. Somebody was talking about the effort trial which is where this inflammation
data came from. The answer to how to go find it really, I think we should go back to the
basics and get simple because in the effort trial, they used a very simple thing that
mostly focused on weight and intake. And then I think one other thing and, and that predicted
response to nourishment. And so I think that that the problem is that we've gotten way
ahead of ourselves on this. That malnutrition means something, it's certainly predicts poor
outcomes, but it doesn't predict who gets response to nourishment and that I think it's
prompted me to actually request in many venues that we call it something else. I have proposed
Matilda; that hasn't gone over well. But the problem is that if somebody has malnutrition,
to say that they're malnourished it may not be accurate because they may be perfectly
well nourished, it's just that they're wasting from inflammation or they're wasting from
disuse or they're wasting from underlying disease. So I'll talk more when it's my turn.
DR. WILLIAM EVANS: Well, there were some really famous studies done
in the late 70s, early 80s in Boston using TPN to feed the critically ill patients. You
can maintain their body weight, I mean through TPN but their muscle mass continues to decline.
DR. CHRISTOPHER DUGGAN: It was all fat. And they were getting 4,000 calories a day intravenously. And that was the proof of principle.
Shouldn't be called hyperpigmentation. Right. DR. NILESH MEHTA: Great point. I think (UNKNOWN) was now in
Boston but wasn't there before talked about CRP as a marker of switching to ableism and
don't bother until it starts dropping. And this was in the 80s and in the surgical world,
people talked about inflammation. And then, of course, Gordon Jensen in the adult section
reminded us of what he's been saying. So this is spot on. Bill, why don't you jump in, where
are you going? You talked a little bit about the deuterated creatinine levels and your
efforts to now have a solid marker of muscle mass. But where do you want to see that go in
terms of a moonshot study? DR. WILLIAM EVANS: Well, I can say right now that we have ten
NIH funded grants using this method in adults. And yet I think where it really needs to be
used is in infants and children because the body composition data for infants and children
is woefully inadequate. So we've just published a paper in boys with Duchenne dystrophy using
this method. And what this method does without if you want to know the details, you can go back
and look at the PowerPoint presentation I've submitted. It measures functional muscle mass
because creatine and creatine phosphate is co-located with the contractual components
of muscle. So it's undiluted by water, it's undiluted by fat, it's undiluted by fibrotic
tissue. And then and we compared boys with DMD to healthy controls, healthy boys. And
we have some teenage boys and one 15 year old non ambulant boy had less than 5% of his body
weight as muscle. So this is the first kind of very simple. When you talk about simple,
you know, measuring the muscle mass that's been widely available and the two studies
that I would be most interested in pediatrics. One is, in talking with my NICU cohorts is
very low birth weight babies tend to develop neurocognitive problems by the time they're
two. And the indicators of neurocognitive status are not great. And the measures of
growth or healthy growth are not great and the ability to supply extra high quality protein
for these babies while they're in the NICU is also not great because you have to obviously
give it along with breast milk. So that's one. I would be really interested in kind
of understanding the role of changes in muscle mass because we can measure muscle mass every
two weeks if we want. You can measure it however you want. It's completely noninvasive and
it's relationship to neurocognitive changes because I maintain that establishing muscle
mass which is a heavily myelinated tissue is absolutely related to the changes that
occur in the central nervous system. And then secondly, and probably the big moonshot, we
know that body mass index of body weight in infants and children is highly associated
with long term outcomes. Obesity being one and chronic diseases being
two and what component of body mass index is most important? We've assumed that it's
fat. I maintain that that's probably not the case, that maybe muscle. Muscle is an extremely
high energy requiring tissue. It is the reason why the basal metabolic rate is different
between boys and girls and men and women, and changes with age and changes with growth.
So I would be really interested in knowing what the body composition and I would predict
muscle is associated with these longer term outcomes related to obesity and and the sequelea
of events following that. (CROSSTALK) DR. NILESH MEHTA: How easy is this to do in living subjects, your technique?
DR. WILLIAM EVANS: We just finished a study in rural Bangladesh using the method, so it's
easy. SPEAKER: How about in patients with advanced renal failure? DR. WILLIAM EVANS: Yeah, as long as they can produce
some urine, it's fine. We look at the enrichment of creatinine, so the method uses a very small
dose of deuterated creatine either encapsulated or for children we dissolve it in a liquid
and we can dissolve it in a little small amount of heavy water.
And we can get from that lean body mass, fat mass, muscle mass from a simple sip of water,
and then we take a saliva and urine sample a little bit later on. And as long as someone
produces some urine, we're looking at the enrichment of creatine and not the amount
of creatine that's excreted. So as I said, we're doing studies in some really sick people
right now. SPEAKER: And just be curious to see if you could do it out of the dialysis or something?
DR. WILLIAM EVANS: Well, it would be a little tricky to be honest. That would be one of the populations. You
have to take it from the blood sample rather than the dialysis. But that's... it's cool,
DR. NILESH MEHTA: Thanks Bill. Let’s keep moving. DR. WILLIAM EVANS: We've outdated against a body MRI. DR. LIAM MCKEEVER: OK, wonderful. DR. TAMARA HANNON: Well, this discussion
is... Do you want me to go ahead? DR. NILESH MEHTA: Yes, please, yeah.
DR. TAMARA HANNON: So I'm a pediatric endocrinologist, so I often see these kids that are fed in
the NICU when they have metabolic problems later in life. So I completely agree that
measures of body fat and growth in the newborn period are, are not what we should rely unnecessarily.
I don't know what they are, but to indicate the best health. In fact, if you have to overfeed
a person for them to store fat as adipose, that's probably not metabolically normal.
Like that's not biologically normal. It's biologically normal for humans to store fat
without being overfed. So I spend a lot of my time thinking about where to intervene
in the cycle of diabetes which you might not think of as a condition of malnourishment.
However, you know, it can be considered malnourishment in the way that most people with diabetes
are not getting a diet, a nourishing diet, a diet that's relatively high in vitamins
and minerals in relationship to the calories. But what I would say from my moonshot study,
here I go. This is out there. But you all know what WIC is? WIC is this
special supplemental nutrition program for women, infants and children. And WIC serves
more than 50% of the infants in the U.S. It is a huge program. And we know that babies
and young women, women of childbearing age who get WIC services have better health outcomes.
We know they have lower rates of obesity. We know that they're better nourished. It's
very burdensome to sign up for WIC. So the first thing I would propose is an implementation
study to allow all nutritionally at risk women, even if they're not pregnant, even pre pregnancy,
why shouldn't an 18 year old who's going to become pregnant at age 21 already be on WIC if we
want to prevent bad outcomes? So the implementation of an easier way for young women of child
bearing age to access a evidence-based program that is managed by the states in many different
WIC offices. So implementing an easier way to get on this and serving women who are at
high risk this lends itself to a multitude of studies of the biology of what happens
when you try to nourish women prior to a pregnancy or during a pregnancy, and what happens to
them after pregnancy and what happens to their infants.
So that's my Moonshot, billions of dollars study design. Now, of course, so that we could we
could suggest anything that we want and not be limited to practicalities. But what if
you did make WIC available to anyone, any woman who was of childbearing age? Not everyone
would sign up for WIC, right? What if you made it really easy? Would still half the
people sign up for WIC, would even half the people sign up for WIC? If we know it works
and we know it lends itself to better outcomes and we know who signs up for WIC, and we can
study what happens to those women and what happens to their babies and what they eat?
What they eat so we can study actually what's distributed to them. That seems about as close
as what I'd want to do for the rest of my career as anything.
DR. NILESH MEHTA: That's a great one. Tamara you're addressing both the mother and child almost at a family
level intervention and an intervention which at a system level is far out, far reaching
and implementation is well said, well-meaning interventions such as this still reach only
a certain fraction. I like it, it's a very, it's not easy as you said, but it's far reaching
and hopefully there will be support to address that. Thank you. We'll come back. We'll have
more. It's Vijay's turn. Vijay, you want to jump in and give us your moonshot study?
DR. VIJAY SRINIVASAN: Oh, sure. Thank you. This is an absolutely wonderful discussion to be
a part of, and I am just so excited and stimulated by all the discussions we're having. In fact,
Moonshot is interesting because I'm just in the middle of our towards the end of finishing
my fourth or fifth read of Apollo 13, which I always sort of am inspired by as probably
the most successful failure and a good metaphor for a lot of what we do in our research and
enterprise. So many...lots of hard work, some successes, but a good deal of failure. But
the more important message that I'm always reminded by that book when I read it every
time is how human ingenuity overcame adversity through the constant sort of adaptation and
sort of tinkering and constant course corrections throughout the whole journey to bring the
astronauts back home safely. So using that metaphor, keeping in mind that I'm a pediatric
intensivist and I deal with the full continuum from acute illness all the way through survivorship,
through chronic technology dependence, which is now my latest sort of place of focus.
It is easy or hard, depending on how you look at it. And I am struck by the fact that I
deal with noise. There is noise from critical illness, there's noise from interventions,
there's noise from all the toxicities. And I'm trying to do an intervention that will
give me a signal that's big enough. And usually, I feel because it is so noisy, the intervention
has to be a massive thump to really generate that signal. And perhaps 50 years ago our
science and care was not as good and we had better signals from smaller interventions
because we were sort of coming up with bigger game changers. So in that difficult context,
I in fact, I'm sort of as I increasingly think I kind of am thinking a little against conventional
wisdom in that should we be thinking about one intervention or should we be thinking
about a bundle of interventions, recognizing that this is about bringing the human, the
child of the adult back to health? So in other words, nutrition coupled with sleep, with
physical rehab, with aspects of nurturing, and how does one implement all that?
Put it together and it doesn't stop at the point of the inpatient, as Tamara just said,
you have to continue it in the outpatient setting and you have to continue the process.
So to me, as I think of this design, how do I sort of bring all these elements of the
bundle together? How can I make sure that practitioners are able to collect data, measure
what they're supposed to do accurately and comparably outcomes such as body function,
composition, muscle mass, the metabolome, the microbiome, neurocognition, development,
and more importantly, to continue this into the families when they go back home. And how
do they sort of maintain and continue to improve on their trajectory, recognizing that many
of the kids that come to the pediatric ICU are repeat offenders, they often have chronic
comorbidities, whether they're NICU graduates, whether they are congenital cardiac malformation,
whether they have neurological impairment. It is a challenge to make sure that they can
sort of reach their full potential, which is sort of it goes back to, again, my earlier
presentation about the sustainable goals and what does it mean for us as an individual
and a population.
DR. NILESH MEHTA: Vijay, that was brilliant. I'm not surprised that in your world, and critical
care, you have challenged the dogma of the silver bullet because there isn't one. And
there are going to be bundled interventions more and more. Well said, that's a very exciting
concept. Anybody want to chime in? Have questions for Vijay?
DR. DAVID SUSKIND: Now, I'd like to second that I think especially in the world that I live in with
inflammatory bowel disease, it isn't just one intervention that works to our children.
We are intervening from a lifestyle standpoint as well as medication therapy, as well as
nutritional therapy, and understanding that one thing by itself may not be that silver
bullet, but really incorporating individuals I suppose, and then studying that, so you
are able to then kind of push the science for or I think is central to all of the conditions
that we focus on. So kudos for that.
DR. NILESH MEHTA: Thank you. You know, we won't, I won't elaborate on this, but this is very
similar to the concept of not just bundling it, but bundling it in relation to the individual's
needs and the whole concept of precision nutrition as well. This is a good time to go to Liam.
Liam, I don't want to put you on the spot, and please feel free to go as deeper, as little
as you want. But you've heard of all these considerations. You've heard of bundling interventions,
you heard of patient populations that are likely to be most beneficial. They might be
tools that might allow you to identify as someone in your position who gets to look
at the literature and try to make sense of it in the form of guiding it, bringing it
back to the bedside. What kind of study? What is your moonshot study design? Where does
this nutritional world going and what would you like to see from researchers when you
sit down and assemble the next panel for guidelines generation?
DR. LIAM MCKEEVER: I mean, we're always our own best critic. And one of the criticisms I have
about the work I do on the guidelines is that we are providing population estimates, and
I don't believe a population estimate is a useful thing. I think that it is a major misleading
thing that I spend a lot of time working on. But, so precision nutrition is actually a
good segway for what I would want to do. I have two things. I want to demonstrate efficacy,
but I want to also be able to account for the fact that we don't...that not everybody
is going to respond to nutrition in the exact same way. I think malnutrition is one of the
starting points for that, right? So I'd like to see an RCT where we demonstrate that intervening
upon our nutritional screenings or assessments actually alters clinical outcome. And to do
that, you have to be looking at people who are fed more than people who are randomized
to be fed less. And that could be standard care versus supplemental PN or standard care
versus an enteral algorithm. I think there are ways to do it that don't
involve starving someone and then just showing that you'd have to have four groups. You'd
have to have people who were designated malnourished. And then you look at the people who are malnourished
and high fed, malnourished and low fed and then not malnourished, high fed, not malnourished,
low fed. And what you're looking for is does... basically, does malnutrition modify the effect
of feeding an outcome? And you would get to that through on interaction term. So it's
a very simple thing that we've never done in RCT that I've seen, at least not in adults.
But I don't think you guys have that in kids either. Then the other thing that I would
add to that is I would be taking genetics data and I would also want to be able to look
at it stratified by the various different illness states. So, as much as someone could
write one giant grant, I think we are not served by having one researcher answer all
those questions. I would rather seek coordination where there are several different principal
investigators who are all kind of working together to answer these questions individually
at different hospitals with their own sensibilities. But certain things held in place to make sure
that when I have to do my job, I can conflate them all into a forest plot. So the interventions
were similarly administered, the outcomes were similarly measured, things like that.
But I think that would be important too, to not have it be this thing where forever we're
all just looking at rehashings of one giant study that someone was allowed to do.
DR. CHRISTOPHER DUGGAN: But how do you avoid the problem with 20 underpowered studies that probably
aren't ethical to accomplish? The power is not there...DR. WILLIAM EVANS: Or even harder to find malnutrition.
DR. LIAM MCKEEVER: You do that with a summary statistics. So that's what a meta-analysis
is about, right?
DR. CHRISTOPHER DUGGAN: ...is not an individual study with the idea of, hey, someday my study is going to
be part of a meta-analysis.
DR. LIAM MCKEEVER: Well, it's not some day. It's part of an organized effort. Everybody knows
up front that we've pulled these different researchers in to do this massive undertaking.
DR. CHRISTOPHER DUGGAN: And to do it as a multi-test, so you're arguing for a multicenter trial?
DR. LIAM MCKEEVER: I am. But it's not one it's not just guided by one person. It's literally
we are splitting things up and we're, so that here's the thing, right? There are so many
little things in any one study design that become problematic. I'd like to diversify
a little bit. If you put it all in the hands of one person, then you end up in situations
where maybe like someone doesn't have enough stock in what they're doing, that it's OK.
For example, a lot of times you have this happening and you're not, you put it in the
hands of a dietitian to run it and you're not paying them anything so they don't have
a horse in the race. Next thing you know, when they are bringing somebody into the study,
they're looking at like things like how long is this person going to be in? How much data
am I going to have to collect? And if you don't have somebody there, someone's research
fellow or somebody who their career is hanging in the midst on that to be close to the study
and really monitoring everything, the value of those smaller studies.
And I'm not talking super small. I mean, each of these can be large studies, but the beauty
of smaller studies is there's somebody there whose life is hanging in the balance. Somebody
there who cares whether or not that data is collected properly, who is watching and monitoring
how things are going down, calling the nurses every day to make sure that they understand
what was given those things. I think when you let something get too big fall apart,
I think that there's a benefit to doing that. But the power is only an issue if that's the
only study in existence and it's too small. We can have them all adequately powered. If
they're in, you know, the truth is, the more you stratify, the less, you know, the more
you increase your power to see, in effect, you decrease the heterogeneity in your sample.
Whereas instead, if you just look at everybody, then you need a huge sample size, right? Because
there's so much heterogeneity in your data.
DR. NILESH MEHTA: Liam, you mentioned... Liam, thank you for that. You mentioned stratification,
Vijay used the word course correction. I'm going to go to Todd next. Todd, there's a lot
to learn from centers like yours. Vanderbilt has been leading in terms of innovative study
designs, and you've accomplished as a group quite a lot. Any comments on adaptive trial
design and where are we with that in terms of adaptive randomization? Adaptive trials?
DR. TODD RICE: Yeah. You know, I think there's promise in adaptive trials, but I think and
somebody may have said this earlier, but I think we have to be really cautious of putting
the cart ahead of the horse. A lot of adaptive trials, you have to have some fundamental
understanding of mechanisms and things that are going on in order to adapt. And, I mean,
you guys have heard me say for a number of years that we took for a long time, at least
in the adult world, this one size fits all approach to nutritional care, especially in
the ICU, but in lots of places. And I think we're now realizing that one size does not
fit all. And an adaptive design can be great for those. But we have to understand how the
people have different sizes and what they need is different things. I think we'll get
there, but I think we still have some learning to go in that regard.
DR. NILESH MEHTA: Todd what's your moonshot study? What are you going to dream about.
DR. TODD RICE: Yeah, I caution people that I'm not a pediatrician and I do adult medicine,
but I'm not sure it's different. For me, I think there are multiple steps in the study.
I'm a clinical trial by design. I do randomized trials. And so for me, it's going to be a randomized
trial. But on the front side, before we put people into the randomized trial, before we
put kids into the randomized trial, we need to understand the differences in their malnutrition
states. And for me, that then leads to different interventions for different folks, for different
kids with different malnutrition states, and studying that in order to. Improve. An outcome
that and I don't know what this is in the pediatric world, but it has to be an outcome
that clinicians care about, that administrators care about, and that kids and families care
about. And we've spent a lot of time in the adult world with the first two parts of those
the clinicians care about and the administrators care about. And we're now understanding that
we need to focus a lot on what the patients care about as their outcome.
And so that's kind of the moonshot. If you listen to me and I'm going to say it again because
I actually kind of fell in love with it in the last three or four days. If you listen
to me on the adult side, we try and do nutritional interventions in a short period of time and
then look at a longer-term outcome and using the personal trainer concept I would love
to see a trial that has a personal nutritionist for every patient, and that personal nutritionist
identifies the needs of that patient and then helps them fulfill those needs, not just that
point in time, but across sort of their illness, their hospitalization into their outpatient
setting. And obviously, it's a real moonshot. And you'd have to have 100 million dietitians
and you'd have to have lots of money. But Dr. Lynch, Dr. Pratt and Dr. Vargas all told us we don't
have any financial constraints. We don't have any logistical constraints. And so we're going for it.
DR. TAMARA HANNON: When you went out and... (CROSSTALK)
DR. NILESH MEHTA: Say that again?
DR. TODD RICE: Sorry, Tamara.
DR. TAMARA HANNON: Well, you would...I mean that would be great, but when I think about what
my patients want and they need, they want to be able to pay for their medicine and they
want to be able to get the food that they need. They want their kids to have a meal at school
that they'll eat, they want, I mean...they want, they can have a coach, a personal nutritionist,
a trainer, but you actually have to give them the food.
DR. TODD RICE: Yeah. Access. Right? Yeah. And I mean, I...you know, we're dreaming big and
there are no constraints. So our coach is in charge of both. All of that, that I put forth and
access.
DR. VIJAY SRINIVASAN: Yeah. I think is.
DR. NILESH MEHTA: And in the modern era, with technology advances, nutrition code doesn't
have to be 100 million dietitians. This could be remotely accessed. And then, as you are
alluding to Tamara, the intervention would be how to then get them the individualized
prescribed nutrition.
DR. TODD RICE: I mean, I don't know if you know this, but, Tamara, I have a little bit of
Indiana backgrounds and the background has a little bit to do with the impact program
in Kenya that is actually kind of stationed from Indiana. And what they actually found
and it's fascinating and it's worked phenomenal is, is that to treat their HIV patients in
Kenya, they actually had to teach the patients how to essentially farm their own food because
food became a higher priority than, even though they were free, the antiretrovirals that were
being provided them either weren't taken or didn't work if you didn't have the right nutritional
support with them. And so they learned this and they spent an inordinate amount of time
and resources to teach their patients how to do farming stuff and how to grow their
own food and access to their own food, so much so that they got so good at it that they
now sell it. And that's an income for them. In addition what they have as their food.
But I think that's what you're talking about, right, is, is that it's not just bringing food
to people, but it's teaching them how to get that access, whether it's doing it themselves
or finding it or having the resources to do it, etc.
DR. VIJAY SRINIVASAN: Well, it's interesting...
DR. TAMARA HANNON: And that's exactly what I'm talking about because when you talk about whole person health, we're
talking about this, this small amount of it, but if you don't have food, you don't have
health. So it doesn't matter how many specialists you have on your team. It's exactly what
I'm talking about.
DR. WILLIAM EVANS: So I was involved in a in a meeting at the Gates Foundation and they were talking
about looking at the effects of increased dietary protein in malnourished children.
So they started giving eggs to the children, but quickly found out the children weren't
getting the eggs. It was going to the parents and wasn't going to the children. And so there's
a lot of infrastructures. So what ended up happening is that they established egg farms
so they could provide adequate nutrition and increased protein to the entire family before
they could actually start to look at the infants and children in the study. So there's a huge
infrastructure. And early on I heard that there isn't any real consensus definition
of what malnutrition is. And I know in the adult world, especially in the geriatrics
world, I've been to daylong meetings trying to define malnutrition. And the only criteria
that everybody agreed to was involuntary weight loss. And so I think it's it can be a moving
target in trying to do RCTs in, "malnourished children" without really specifying what the
specific nutrient you're interested in and the component of malnutrition that you're
most interested in trying to correct.
DR. VIJAY SRINIVASAN: Yeah, I just you know, (CROSSTALK) With the... Sorry, Nilesh. Just a quick comment
about, you know, there's actually a potential opportunity for a natural experiment that's
already kind of happened. And I kind of want to sort of extrapolate from Tamara's point
about it's about availability and access. All the information advice we give our families
will not matter if you don't have access. And one of the things that we have just done
is expand the child tax credit. And we know that has lifted a lot of children out of poverty.
And I wonder if there is more granular data to see what the nutritional status also did
during this period when they had more sort of access. Because every parent I mean, invariably
they all want to do the right thing for their child, which is to put food on the table and
feed them. And if they had the resources, they would do so. And I think that's one of
those things where I'd be very interested to see if, you know, NIH and other organizations
can actually tease out some of that data to see if it made a difference.
DR. NILESH MEHTA: Great point, Vijay.
DR. DAVID SUSKIND: And that's a really interesting point. But I think. Oh. Sorry. But I think to the
other way of you.... with psychosocial and lifestyle issues are there and having the
money to get the proper foods are important but what are those proper foods and what are
their purchasing power with that so you know as we and many of the sessions have talked
about, you know, malnutrition can be under nutrition and over nutrition. So we may be
providing the economic ability to get the foods, which is an essential component to
it, but also the educational component. The knowledge that programs like WIC provide are
essential to do so. So I think it kind of underlies the difficulty with knowing any
intervention and what is the outcome that we're looking for because it is such a complex
issue and trying to find that granular outcomes with a very broad stroke I think can be difficult
if not done in a very prospective way.
DR. NILESH MEHTA: Thanks, David. David Seres, do you want to have the last word?
DR. DAVID SERES: Sure, I get to say all the above, plus a couple of things. Ron [inaudible] once wrote:
The only nutritional outcomes are mortality, health, economics, and quality of life. And
I kind of hold to that because one of the things that really matter and then there are
things on the way to those things that really matter too. So we could incorporate those.
But we really need to agree on what it is that we're talking about when we use the term
malnutrition, Are we really talking about people who are malnourished? And if we are,
then we need to change our definitions because they include all of these things that are
disease-related and disease epiphenomena. If I were designing a moonshot study, it would
go as follows: given an uninhibited funding policy and everything else, that since we
have become very clear about the fact that we really don't know when, how much, and what
to feed and what route to feed people who are sick. Now, I don't want to...the free hospital
issues. Let's take those off the table now. But for the sick patient, we don't know the
answer to any of those things. Therefore, no patients should be fed in the hospital
unless they're part of a randomized controlled trial. This is my Moonshot because we need
a huge study. We need an enormous study. And what we are really dealing with is something
that truly is experimental and we are experimenting on every single patient that we treat with
nourishment. Now, there is no controversy that if you live long enough and get fed less
enough, you will die of starvation or the consequences of the starvation will worsen
your outcomes. There's no controversy over that. The controversy is how long do you have
to starve? What can you do to change that starvation? Is what we're describing as malnutrition,
starvation, or is it something else? I think it's a lot of things put together. And I said
at the previous Moonshot that I also want to put all of the cachexia, sarcopenia development,
and malnutrition, folks in a room and kind of knock their heads together and say, "Guys,
aren't you talking about something that's almost exactly the same with different, slightly
different inputs?" But all of them have multiple inputs. You can develop cachexia by wasting
or you can develop cachexia by starving. You can develop malnutrition in both of those
ways. So we have to really start thinking not as nutrition interventionalists because
that's a conflict of interest in on all of our parts. All of us want to intervene with
nutrition. So as I think Chris mentioned, we need to pull in people who are content
experts in the diseases rather than in the nutrition who are not wanting to look for
things to do. I've always said that being an expert is one of the most insidious forms
of conflict of interest, because if I don't know what to do or if I have nothing to do,
then you're not going to come talk to me and treat me like an expert. And there goes my
salary and my ego gratification and all of the fun things I get to do when I go to work
every day. So I really have a vested interest in being able to do something. So I've backed
off on all of my thoughts about modifying disease. When I first started in practice,
I thought I was going to put every patient in the hospital on some form of parenteral support
because I was going to stamp out hospital malnutrition.
I'm serious. That was my goal based on what I had been taught. And thankfully I had some
people knock my head around a little bit and say "Excuse me, there's no data for that." And now what
I am doing as a practitioner is trying my best to safely treat starvation. And I don't
know how to do that, and it drives me nuts. So we need to start from simple. We need to
look very simply at the smallest criteria that we can find that actually predicts response
to nourishment in whatever group that we're talking about in every group, every group
is going to be different. So in the effort, trial, inflammation clearly predicted a non-response,
but starvation predicted a response. So, we need to go back to the very finest basics
and do the finest granular studies that we can possibly do where we randomize all of
these things to be able to chop out what it is that actually determines whether or not
somebody is starving, meaning that they have... that they are becoming malnourished or developing
malnutrition in the truest sense that I want it to be.
And then all of these other things that are wasting due to inflammation and so forth,
which have nothing to do with us as nutrition interventionalists. I think I got at all.
DR. NILESH MEHTA: David, That was fantastic. You know, one hour is not enough with such a panel.
I can't tell you how enjoyable this is. And I don't know if this is legit. And maybe Ashley
can tell us and then maybe they can ignore it. But if you had thoughts that you were
unable to fit in here, do send an email and it would be nice to consolidate some of your
thoughts that we were unable to get to. But it is time for me to close this out so that
we have the next 10 minutes to award the poster winners and then close this workshop. This
means I have to offer my sincere thanks right now to this panel. It's been truly...
DR. DAVID SERES: Ashley said Yes, please. Ashley said (CROSSTALK). Send emails with your ideas
and your comments. Hopefully, the organizers will be getting together to write up proceedings,
to publish, and these are things that could be taken into consideration. And I thank my
co-organizers and this has just been a real pleasure. I'm sorry Nilesh back to you.
DR. NILESH MEHTA: Thank you...thank you to Tamara Hannon, David Susskind, Bill Evans, Chris Duggan,
Vijay Srinivasan, Liam McKeever, thank you to Todd Rice and David Seres. At this point,
our Session 11 is closed and we will move to the closing and adjournment of day five.
Thank you all. Have a great evening.
[pause] Alright.
SPEAKER: Great job man. You’re the best. You really are smooth. (Inaudible). You’re so [inaudible] smooth.
DR. NILESH MEHTA: Sorry. Oh, thank you.
SPEAKER: (UNKNOWN) Do we go somewhere else for the poster sessions?
DR. NILESH MEHTA: I am looking for some guidance here. Ashley, do you know if Brett is still
here? Is he going to close the sessions or (UNKNOWN) you going to close the session and
move to the slide for the poster awards? There you are. Am I live?
DR. TAMARA HANNON: Yes, you are, I believe.
DR. NILESH MEHTA: Oh, thank you. Thank you all. Thank you to the participants. Thank you to
the many poster presentation submissions that we had. At this point, I'd like to announce
our poster winners in the next few slides. In the next slide, I will just allude to the
criteria for the poster awardees. These posters, we received to see posters from around the world.
This was really fun to review and NIH scientific committee review these and score them for
these outlined criteria. The purpose is clearly stated. The methods and data are present,
the quality of presented results are evaluated, appropriateness of the conclusions based on
the results and the innovation, and the potential impact of the results. So based on that, let's
now look at the next slide for the adult poster winners. It is my pleasure to announce that
the Adults Track Awards for the posters were awarded to these three individuals, Nina Rocca
and her group who presented... who submitted the work on implementation of a TOC nutrition
intervention for patients with malnutrition. Thank you. Congratulations to Nina and her
group. The next one is Brooke Helleher from University of New Hampshire. And Brooke and
her group submitted their work on the Home Food environment and its association with
age, food security and obesity, co-morbidities in Hispanics and Latino populations. Congratulations
to Brooke Helleher and her group. The next one is Lynne Hiller from James Valley Veteran
Hospital, Florida Estimation of 24-Hour Urinary Creatinine Excretion to Identify Malnutrition
in critically ill Veterans. Congratulations to all three winners in the Adult Track Awards
and thank you for your work. Congratulations and good luck for your future projects. Next,
we will go to the Pediatric FAC Award winners for the posters. On the next slide, the pediatric
awards go to first Nicole Gilbert from Alberta Health Sciences, their work on assessing the
prevalence of malnutrition in pediatric Canadian pediatric hospitals. They evaluated the application
of the AND ESPEN definitions. Congratulations and Nicole and group. And
then Katherine Bell from Brigham and Women's Hospital, Harvard Medical School. Their work
on preterm infant nutritional status comparing to body composition references. Congratulations
Katherine Bell and colleagues. And then finally, Laura Gollins from Texas Children's Hospital
Baylor, where they presented their work on mid-upper arm circumference as
a predictor of body composition in extremely low birth weight infants. Congratulations
to all three award winners in the pediatric or the early life track. And good luck with
your future projects. Next slide. The Scientific Consulting Group will contact all the awardees,
and if you don't hear from them, please reach out to Mark Dennis in the next two... If you
don't hear from them in the next two weeks. Thank you so much. At this point, we are close
to the end of the hour and it remains simply for me to give heartfelt thanks to all of
you for joining over the last two days. And then on the three days before the week before
in the Adult Malnutrition workshop, we've experienced a variety of topics expertly presented
by some of the leading minds and researchers in the group, and it allowed us to highlight
gaps. I thoroughly enjoyed the last session where
with the panel, expert panel we talked about moonshot studies, just as we did in the adult
session last week. My sincere thanks to the NIH, the scientific group led by the leadership
of Christopher Lynch, Charlotte Pratt, and Ashley Vargas. Your involvement, your dedication,
and your investment into the future of malnutrition research is, is really inspiring. And thank
you for providing us with this platform and for all of us to join and put our heads together
on where we go next. The goal of this workshop was to improve outcomes by preventing malnutrition,
and we went from an entire spectrum of early life to adults. And finally, I would say thanks
on behalf of my co-chairs, Dr. Gail Cresci, David Seres, and Dr. Todd Rice. It's been
a pleasure. I wish you all the very best and have a great evening and good luck in all
your research endeavors. Goodbye and good luck.