# Clinical Decision Support System to Optimize Neonatal Nutrition and Growth

> **NIH NIH R41** · MEDICAL PREDICTIVE SCIENCE CORPORATION · 2022 · $261,158

## Abstract

Project Summary/Abstract: Clinical Decision Support System to Optimize Neonatal Nutrition and
Growth
Nutrition, 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). Inadequate
nutrition is associated with growth and neurodevelopmental impairment, and increased rates of both
retinopathy of prematurity and bronchopulmonary dysplasia. Despite the recognized importance of nutrition
and growth, clinicians often fail to deliver the recommended intake with large deficits accruing during
hospitalization. Indeed, 50% of very low birth weight (VLBW, birth weight <1500g) infants leave the NICU at a
discharge weight <10th percentile for their corrected, postnatal age. We have determined that the majority of
NICUs affiliated with the Children’s Hospital Neonatal Consortium, a group of US and Canadian children’s
hospitals, lack Clinical Decision Support Systems (CDSS) to calculate nutrition intake. Moreover, of the
institutions with any CDSS to calculate caloric intake received, few could automatically calculate nutrition
intake from both parenteral and enteral sources without additional copying of data. Clinicians need data on
both 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) and
balance judicious fluid management with optimal nutrition delivery. The goal of this project is to develop a novel
growth and nutrition dashboard, and model projected growth based on nutrition intake and physiologic data
from the multiparameter monitor. We hypothesize that presenting real-time, comprehensive nutrition and fluid
intake data from both parenteral and enteral sources alongside growth modelling will improve clinicians’ ability
to deliver high quality neonatal nutrition and achieve optimal growth. Improvements in nutrition are expected
from an enhanced situational awareness of the intake that an infant has already received, the cumulative
intake that an infant will receive from various nutrition practices, and modelling that accounts for heart rate
activity, a surrogate of energy expenditure.

## Key facts

- **NIH application ID:** 10478336
- **Project number:** 1R41HD109038-01
- **Recipient organization:** MEDICAL PREDICTIVE SCIENCE CORPORATION
- **Principal Investigator:** William E King
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $261,158
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10478336

## Citation

> US National Institutes of Health, RePORTER application 10478336, Clinical Decision Support System to Optimize Neonatal Nutrition and Growth (1R41HD109038-01). Retrieved via AI Analytics 2026-06-16 from https://api.ai-analytics.org/grant/nih/10478336. Licensed CC0.

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