Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants
Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants

Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants

J Perinatol. 2025 Aug 20. doi: 10.1038/s41372-025-02390-2. Online ahead of print.

ABSTRACT

OBJECTIVES: To identify clinical variables and indicators associated with pulmonary hemorrhage in preterm infants.

METHODS: This case-control study included inborn infants <32 weeks. Data were collected in 12-h epochs from birth until hemorrhage onset or up to 72 h for controls. Machine learning used the Random Forest algorithm. Statistical analysis included T test and Mann-Whitney U test.

RESULTS: Among 1133 screened infants, 35 had hemorrhage. Mean gestational age was 25.6 ± 1.6 weeks, birth weight 753 ± 224 g, and median onset of hemorrhage was 44.5 h. Affected infants more often required chest compressions and invasive ventilation. Machine learning (accuracy = 83%, AUC = 90%) identified repeated surfactant dosing and postnatal hypotension in the first 12 h of life as top predictors, along with maternal and gestational age. Mortality was higher in cases than controls (19% vs. 3%, p = 0.005).

CONCLUSION: Repeated surfactant dosing and early postnatal hypotension are key predictors for pulmonary hemorrhage in preterm infants.

PMID:40836119 | DOI:10.1038/s41372-025-02390-2