ArticleJournal of perinatology : official journal of the California Perinatal Association2025
Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants.
Article in Journal of perinatology : official journal of the California Perinatal Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Does "POWS" pack a powerful enough punch to predict pulmonary hemorrhage in preterm infants?Pediatric research · 2026Article
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Authors and funding
6 authors.
Funding
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Abstract
objectivesTo identify clinical variables and indicators associated with pulmonary hemorrhage in preterm infants.
methodsThis 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.
resultsAmong 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).
conclusionRepeated surfactant dosing and early postnatal hypotension are key predictors for pulmonary hemorrhage in preterm infants.
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