Murray Anne L, O'Boyle Daragh S, Walsh Brian H, Murray Deirdre M
Cork University Maternity Hospital, Wilton, Cork, Ireland.
INFANT Centre, Paediatric Academic Unit, Cork University Hospital, Wilton, Cork, Ireland.
Arch Dis Child Fetal Neonatal Ed. 2025 Apr 17;110(3):279-284. doi: 10.1136/archdischild-2024-327366.
To validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data.
Secondary review of electronic health record data of term deliveries from January 2017 to December 2021.
A tertiary maternity hospital.
Infants >36 weeks' gestation with the following clinical variables available: Apgar Score at 1 min and 5 min, postnatal pH, base deficit, and lactate values taken within 1 hour of birth INTERVENTIONS: Previously trained open-source logistic regression and random forest (RF) prediction algorithms were used to calculate a probability index (PI) for each infant for the occurrence of HIE.
Validation of a machine learning algorithm to identify infants at risk of HIE in the immediate postnatal period.
1081 had a complete data set available within 1 hour of birth: 76 (6.95%) with HIE and 1005 non-HIE. Of the 76 infants with HIE, 37 were classified as mild, 29 moderate and 10 severe. The best overall accuracy was seen with the RF model. Median (IQR) PI in the HIE group was 0.70 (0.53-0.86) vs 0.05 (0.02-0.15), (p<0.001) in the non-HIE group. The area under the receiver operating characteristics curve for prediction of HIE=0.926 (0.893-0.959, p<0.001). Using a PI cut-off to optimise sensitivity of 0.30, 936 of the 1081 (86.5%) infants were correctly classified.
In a large unseen data set an open-source algorithm could identify infants at risk of HIE in the immediate postnatal period. This may aid focused clinical examination, transfer to tertiary care (if necessary) and timely intervention.
验证一种缺氧缺血性脑病(HIE)预测算法,以利用现成的临床数据在出生后立即识别有HIE风险的婴儿。
对2017年1月至2021年12月足月分娩的电子健康记录数据进行二次回顾。
一家三级妇产医院。
孕周>36周且有以下临床变量的婴儿:1分钟和5分钟时的阿氏评分、产后pH值、碱缺失以及出生后1小时内测得的乳酸值。干预措施:使用先前训练的开源逻辑回归和随机森林(RF)预测算法为每个婴儿计算HIE发生的概率指数(PI)。
验证一种机器学习算法,以识别出生后即刻有HIE风险的婴儿。
1081例婴儿在出生后1小时内有完整数据集:76例(6.95%)患有HIE,1005例无HIE。在76例患有HIE的婴儿中,37例为轻度,29例为中度,10例为重度。RF模型的总体准确率最高。HIE组的PI中位数(IQR)为0.70(0.53 - 0.86),非HIE组为0.05(0.02 - 0.15),(p<0.001)。预测HIE的受试者工作特征曲线下面积为0.926(0.893 - 0.959,p<0.001)。使用PI临界值0.30以优化敏感性,1081例婴儿中有936例(86.5%)被正确分类。
在一个大型的未见过的数据集里,一种开源算法可以识别出生后即刻有HIE风险的婴儿。这可能有助于有针对性的临床检查、转至三级医疗(如有必要)以及及时干预。