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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

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