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Personalized weight change prediction in the first week of life
Mélanie Wilbaux1, Severin Kasser2, Julia Gromann2
1Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital (UKBB), Basel, Switzerland.
Insights
This study developed a new mathematical model to accurately predict infant weight changes in the first week of life, considering various neonatal and maternal factors. The model also quantifies the impact of supplemental feeding on infant weight gain.
Area of Science:
- Neonatal physiology
- Mathematical modeling in medicine
- Pediatric nutrition
Background:
- Neonatal weight change (loss and gain) is highly variable.
- Traditional weight nomograms have limited value for identifying at-risk neonates.
- Factors influencing neonatal weight change require further characterization.
Purpose of the Study:
- To model weight changes in late preterm and term neonates during the first week of life.
- To identify and quantify neonatal and maternal factors affecting weight change.
- To develop an online tool for personalized infant weight prediction.
Main Methods:
- Prospective longitudinal weight data collection from 3638 neonates up to 7 days.
- Development of a semi-mechanistic model using nonlinear mixed-effects modeling (NONMEM 7.3).
- Model evaluation on a separate cohort of neonates.
Main Results:
- The model demonstrated high predictive performance for individual infant weight change (bias 0.15%, precision 1.43%).
- Accurate prediction of weight change and supplemental feeding effects up to 1 week post-birth.
- Key predictors include birth weight, gestational age, gender, delivery mode, feeding type, maternal age, and parity.
Conclusions:
- The study presents the first mathematical model for describing neonatal weight change.
- The model offers an educational online tool for personalized infant weight prediction.
- This tool aids in monitoring and managing infant weight in the first week of life.
Background & Aims:
Almost all neonates show physiological weight loss and consecutive weight gain after birth. The resulting weight change profiles are highly variable as they depend on multiple neonatal and maternal factors. This limits the value of weight nomograms for the early identification of neonates at risk for excessive weight loss and related morbidities. The objective of this study was to characterize weight changes and the effect of supplemental feeding in late preterm and term neonates during the first week of life, to identify and quantify neonatal and maternal influencing factors, and to provide an educational online prediction tool.
Methods:
Longitudinal weight data from 3638 healthy term and late preterm neonates were prospectively recorded up to 7 days of life. Two-thirds (n = 2425) were randomized to develop a semi-mechanistic model characterizing weight change as a balance between time-dependent rates of weight gain and weight loss. The dose-dependent effect of supplemental feeding on weight gain was characterized. A population analysis applying nonlinear mixed-effects modeling was performed using NONMEM 7.3. The model was evaluated on the remaining third of neonates (n = 1213).
Results:
Key population characteristics (median [range]) of the whole sample were gestational age 39.9 [34.4-42.4] weeks, birth weight 3400 [1980-5580] g, maternal age 32 [15-51] years, cesarean section 26%, and girls 50%. The model demonstrated good predictive performance (bias 0.01%, precision 0.56%), and is able to accurately predict individual weight change (bias 0.15%, precision 1.43%) and the dose-dependent effects of supplemental feeding up to 1 week after birth based on weight measurements during the first 3 days of life, including birth weight, and the following characteristics: gestational age, gender, delivery mode, type of feeding, maternal age, and parity.
Conclusions:
We present the first mathematical model not only to describe weight change in term and late preterm neonates but also to provide an educational online tool for personalized weight prediction in the first week of life.
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