What type of study is this?

This is a Case Report study (also classified as: Observational).

September 10, 2025Journal of Perinatology0 citationsOpen Access

Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants

HAHany Aly VNVanishree Nandakumar HÇHasan Çetin

Key Points

Machine learning accurately predicts pulmonary hemorrhage, indicating a need for better monitoring in preterm infants.
Among 1133 screened infants, 35 developed pulmonary hemorrhage, with an accuracy of 83% in prediction.
Case-control analysis used identified clinical variables from machine learning, involving repeated surfactant dosing and postnatal hypotension.
Mortality was significantly higher in infants with hemorrhage compared to controls, emphasizing the importance of early detection.

Abstract

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.

Ask AI

Helpful

Bookmark

View Full Paper