Medical Predictive Science Corporation — Department of Health and Human Services STTR Phase I: NICHD

Description

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 andlt;1500g) infants leave the NICU at a discharge weight andlt;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.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.