Clinical evaluation of a novel preterm standardised parenteral nutrition system for targeted macronutrient delivery

BACKGROUND: System-based approaches to operationalise international nutrition recommendations for preterm infants do not exist. We developed the PremSmart (Preterm Standardised Multimodal and Responsive Transitional) nutrition system, comprising standardised parenteral nutrition (SPN) formulations and an enteral feed volume-based protocol using in silico modelling of macronutrient data to address this gap. This study evaluated the system's effectiveness in delivering macronutrient intakes within target ranges. METHODS: Prospectively collected macronutrient intake data from 49 infants ( < 34 wk gestation and ≤1500 g; n = 14 < 28 wk) who received the PremSmart system were compared with modelled intakes and predefined target ranges across two nutrition epochs: day of life 1-4 to evaluate incremental macronutrient advancement, and the transition phase, analysed across advancing enteral feed volumes (10 mL/kg/day increments). RESULTS: The system delivered amino acid, glucose and lipid intakes within target ranges across both nutrition epochs in <28wk and ≥28wk gestation subgroups, consistent with the modelled design. During days 1-4, median macronutrient intakes advanced incrementally within target ranges, and remained within target ranges throughout the transition phase. Overall daily amino acid target attainment was 88%, with no excessive intakes. SPN accounted for 97% of all parenteral nutrition days. CONCLUSIONS: Nutrient modelling enables data-informed SPN that achieves precise and safe macronutrient delivery. IMPACT: This is the first clinical study using modelling of historical macronutrient intake data to develop targeted standardised parenteral nutrition (SPN) products for preterm infants, addressing an unmet clinical need. It provides proof-of-concept evidence that a multimodal SPN system, comprising a starter and follow-on aqueous-SPN suite, an intravenous lipid emulsion, and an enteral feed volume-based protocol can achieve macronutrient delivery within predefined target ranges across the transition from parenteral to enteral nutrition in a diverse preterm cohort. Data-driven approaches such as nutrient modelling should be leveraged to design SPN for preterm infants capable of delivering precise nutrition and ensuring patient safety.

Authors

Institutions

Publication Details

Journal
Pediatric Research
Published
2026-06-17
DOI
https://doi.org/10.1038/s41390-026-05167-1
Primary Topic
Clinical Nutrition and Gastroenterology
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinical evaluation of a novel preterm standardised parenteral nutrition system for targeted macronutrient delivery

Ann‐Marie Brennan, Sarah E. Fenton, Lavinia S. Panaite, Mairead E. Kiely et al.
Pediatric Research
Clinical Nutrition and Gastroenterology
article

Clinical evaluation of a novel preterm standardised parenteral nutrition system for targeted macronutrient delivery

Ann‐Marie Brennan, Sarah E. Fenton, Lavinia S. Panaite, Mairead E. Kiely, Brendan P. Murphy
article en

Abstract

BACKGROUND: System-based approaches to operationalise international nutrition recommendations for preterm infants do not exist. We developed the PremSmart (Preterm Standardised Multimodal and Responsive Transitional) nutrition system, comprising standardised parenteral nutrition (SPN) formulations and an enteral feed volume-based protocol using in silico modelling of macronutrient data to address this gap. This study evaluated the system's effectiveness in delivering macronutrient intakes within target ranges. METHODS: Prospectively collected macronutrient intake data from 49 infants ( < 34 wk gestation and ≤1500 g; n = 14 < 28 wk) who received the PremSmart system were compared with modelled intakes and predefined target ranges across two nutrition epochs: day of life 1-4 to evaluate incremental macronutrient advancement, and the transition phase, analysed across advancing enteral feed volumes (10 mL/kg/day increments). RESULTS: The system delivered amino acid, glucose and lipid intakes within target ranges across both nutrition epochs in <28wk and ≥28wk gestation subgroups, consistent with the modelled design. During days 1-4, median macronutrient intakes advanced incrementally within target ranges, and remained within target ranges throughout the transition phase. Overall daily amino acid target attainment was 88%, with no excessive intakes. SPN accounted for 97% of all parenteral nutrition days. CONCLUSIONS: Nutrient modelling enables data-informed SPN that achieves precise and safe macronutrient delivery. IMPACT: This is the first clinical study using modelling of historical macronutrient intake data to develop targeted standardised parenteral nutrition (SPN) products for preterm infants, addressing an unmet clinical need. It provides proof-of-concept evidence that a multimodal SPN system, comprising a starter and follow-on aqueous-SPN suite, an intravenous lipid emulsion, and an enteral feed volume-based protocol can achieve macronutrient delivery within predefined target ranges across the transition from parenteral to enteral nutrition in a diverse preterm cohort. Data-driven approaches such as nutrient modelling should be leveraged to design SPN for preterm infants capable of delivering precise nutrition and ensuring patient safety.

Pediatric Research
University College Cork (IE), University Hospital Waterford (IE), Cork University Hospital (IE), Infant (IE)
Openalex Percentile: Top 10%
Clinical Nutrition and Gastroenterology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.