利用人工智能预测早产儿肺出血。
Leveraging artificial intelligence for prediction of pulmonary hemorrhage in preterm infants.
作者信息
Aly Hany, Nandakumar Vanishree, Cetin Hasan, Eltaly Hatem, Nesterenko Tetyana, Mohamed Mohamed A
机构信息
Division of Neonatology, Cleveland Clinic, Cleveland, OH, USA.
Cleveland Clinic Foundation, Cleveland, OH, USA.
出版信息
J Perinatol. 2025 Aug 20. doi: 10.1038/s41372-025-02390-2.
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.
目的
确定与早产儿肺出血相关的临床变量和指标。
方法
本病例对照研究纳入孕周小于32周的足月儿。从出生至出血发作期间，以12小时为时间段收集数据，对照组则收集至出生后72小时。机器学习采用随机森林算法。统计分析包括T检验和曼-惠特尼U检验。
结果
在1133名筛查婴儿中，35名发生出血。平均胎龄为25.6±1.6周，出生体重753±224克，出血中位发作时间为44.5小时。受影响的婴儿更常需要胸外按压和有创通气。机器学习（准确率=83%，AUC=90%）确定出生后12小时内重复使用表面活性剂和产后低血压是主要预测因素，同时还有母亲因素和胎龄。病例组的死亡率高于对照组（19%对3%，p=0.005）。
结论
重复使用表面活性剂和产后早期低血压是早产儿肺出血的关键预测因素。
Zotero 插件