/: Clinical Decision Support System to Optimize Neonatal Nutrition andGrowthNutrition, defined as energy, macronutrients (protein, fat, and carbohydrates), and micronutrients (e.g.,electrolytes), is a critical feature of care for preterm infants in the neonatal intensive unit (NICU). Inadequatenutrition is associated with growth and neurodevelopmental impairment, and increased rates of bothretinopathy of prematurity and bronchopulmonary dysplasia. Despite the recognized importance of nutritionand growth, clinicians often fail to deliver the recommended intake with large deficits accruing duringhospitalization. Indeed, 50% of very low birth weight (VLBW, birth weight <1500g) infants leave the NICU at adischarge weight <10th percentile for their corrected, postnatal age. We have determined that the majority ofNICUs affiliated with the Children's Hospital Neonatal Consortium, a group of US and Canadian children'shospitals, lack Clinical Decision Support Systems (CDSS) to calculate nutrition intake. Moreover, of theinstitutions with any CDSS to calculate caloric intake received, few could automatically calculate nutritionintake from both parenteral and enteral sources without additional copying of data. Clinicians need data onboth nutrition and fluid intake to consider the trade-offs associated with various nutrition delivery practices(e.g., parenteral nutrition, intravenous lipid emulsions, enteral fortification, and central line placement) andbalance judicious fluid management with optimal nutrition delivery. The goal of this project is to develop a novelgrowth and nutrition dashboard, and model projected growth based on nutrition intake and physiologic datafrom the multiparameter monitor. We hypothesize that presenting real-time, comprehensive nutrition and fluidintake data from both parenteral and enteral sources alongside growth modelling will improve clinicians' abilityto deliver high quality neonatal nutrition and achieve optimal growth. Improvements in nutrition are expectedfrom an enhanced situational awareness of the intake that an infant has already received, the cumulativeintake that an infant will receive from various nutrition practices, and modelling that accounts for heart rateactivity, a surrogate of energy expenditure.
Public Health Relevance Statement: Project narrative: Clinical Decision Support System to Optimize Neonatal Nutrition and Growth
Clinicians are challenged to provide adequate nutrition to preterm and critically ill term infants in a Neonatal
Intensive Care Unit, contributing to growth restriction in the majority of infants with lifelong consequences.
While many of these challenges are physiological, others are systematic and include clinicians' inability to
adequately assess nutritional intake from combined parenteral and enteral sources, and inability to identify true
growth versus fluid retention, among many other causes. We will develop a novel dashboard that harvests data
from the electronic health record, calculates actual nutrition intake the infant has received and projected
nutrition intake the infant will receive, and models growth using novel algorithms allowing clinicians to improve
nutrition delivery, growth, and outcomes.
Project Terms:
