Predicting serum phosphate levels in very preterm infants using machine learning

Area of Science:

• Neonatal Medicine
• Biostatistics
• Machine Learning Applications

Background:

• Hypophosphatemia is prevalent in very preterm and small for gestational age (SGA) infants, leading to severe complications.
• Current diagnosis relies on direct serum or plasma phosphate measurements.
• Machine learning (ML) offers potential for indirect phosphate estimation using routine clinical and blood gas data.

Purpose of the Study:

• To analyze first-week electrolyte concentrations in very preterm infants within the ImNuT-trial.
• To develop and externally validate ML models for estimating serum phosphate levels in the first postnatal week.
• To utilize routinely collected clinical and nutritional data for phosphate estimation in preterm infants.

Main Methods:

• Retrospective analysis of 120 infants (<29 weeks gestation) under a standardized nutritional protocol.
• Description of electrolyte trajectories (calcium, potassium, sodium, phosphate) stratified by hypophosphatemia and SGA status.
• Development of ML models (elastic net, gradient boosting, random forests, etc.) for concurrent and future phosphate prediction (12h, 24h) using 22 candidate predictors and cross-validation, including an external cohort of 40 infants.

Main Results:

• 33.6% of 119 infants developed hypophosphatemia in the first week.
• SGA infants exhibited significantly higher and earlier risk of hypophosphatemia (log-rank p < 0.0001).
• Elastic net models demonstrated best external performance, consistently using calcium, potassium, gestational age, SGA status, and weight z-scores as predictors.

Conclusions:

• SGA status is a critical factor for early hypophosphatemia in very preterm infants.
• ML models can moderately and consistently estimate serum phosphate levels 12-24 hours ahead using available data.
• These ML models may serve as screening tools to optimize nutritional support and reduce blood sampling, pending prospective validation.