Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK.
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK.
Lancet Digit Health. 2024 Apr;6(4):e238-e250. doi: 10.1016/S2589-7500(23)00267-4.
Affecting 2-4% of pregnancies, pre-eclampsia is a leading cause of maternal death and morbidity worldwide. Using routinely available data, we aimed to develop and validate a novel machine learning-based and clinical setting-responsive time-of-disease model to rule out and rule in adverse maternal outcomes in women presenting with pre-eclampsia.
We used health system, demographic, and clinical data from the day of first assessment with pre-eclampsia to predict a Delphi-derived composite outcome of maternal mortality or severe morbidity within 2 days. Machine learning methods, multiple imputation, and ten-fold cross-validation were used to fit models on a development dataset (75% of combined published data of 8843 patients from 11 low-income, middle-income, and high-income countries). Validation was undertaken on the unseen 25%, and an additional external validation was performed in 2901 inpatient women admitted with pre-eclampsia to two hospitals in south-east England. Predictive risk accuracy was determined by area-under-the-receiver-operator characteristic (AUROC), and risk categories were data-driven and defined by negative (-LR) and positive (+LR) likelihood ratios.
Of 8843 participants, 590 (6·7%) developed the composite adverse maternal outcome within 2 days, 813 (9·2%) within 7 days, and 1083 (12·2%) at any time. An 18-variable random forest-based prediction model, PIERS-ML, was accurate (AUROC 0·80 [95% CI 0·76-0·84] vs the currently used logistic regression model, fullPIERS: AUROC 0·68 [0·63-0·74]) and categorised women into very low risk (-LR <0·1; eight [0·7%] of 1103 women), low risk (-LR 0·1 to 0·2; 321 [29·1%] women), moderate risk (-LR >0·2 and +LR <5·0; 676 [61·3%] women), high risk (+LR 5·0 to 10·0, 87 [7·9%] women), and very high risk (+LR >10·0; 11 [1·0%] women). Adverse maternal event rates were 0% for very low risk, 2% for low risk, 5% for moderate risk, 26% for high risk, and 91% for very high risk within 48 h. The 2901 women in the external validation dataset were accurately classified as being at very low risk (0% with outcomes), low risk (1%), moderate risk (4%), high risk (33%), or very high risk (67%).
The PIERS-ML model improves identification of women with pre-eclampsia who are at lowest and greatest risk of severe adverse maternal outcomes within 2 days of assessment, and can support provision of accurate guidance to women, their families, and their maternity care providers.
University of Strathclyde Diversity in Data Linkage Centre for Doctoral Training, the Fetal Medicine Foundation, The Canadian Institutes of Health Research, and the Bill & Melinda Gates Foundation.
子痫前期影响 2-4%的妊娠，是全球孕产妇死亡和发病的主要原因。本研究旨在利用常规可获得的数据，开发并验证一种新的基于机器学习且适应临床环境的疾病发生时间模型，以便在出现子痫前期的女性中排除和预测不良母婴结局。
我们使用了来自首次评估子痫前期之日的卫生系统、人口统计学和临床数据，以预测在 2 天内发生母死亡率或严重发病率的 Delphi 综合结局。我们使用机器学习方法、多重插补和十折交叉验证在一个发展数据集（来自 11 个低收入、中等收入和高收入国家的 8843 例患者的已发表数据的 75%）上拟合模型。在未见过的 25%的数据集上进行验证，并在英格兰东南部两家医院收治的 2901 例子痫前期住院女性中进行了额外的外部验证。预测风险准确性由受试者工作特征曲线下面积（AUROC）确定，风险类别由负（-LR）和正（+LR）似然比确定。
在 8843 名参与者中，590 名（6.7%）在 2 天内发生了复合不良母婴结局，813 名（9.2%）在 7 天内，1083 名（12.2%）在任何时间发生。一个基于 18 个变量随机森林的预测模型 PIERS-ML 是准确的（AUROC 0.80 [95%CI 0.76-0.84]，与目前使用的逻辑回归模型全 PIERS：AUROC 0.68 [0.63-0.74]），并将女性分为极低风险（-LR <0.1；1103 名女性中的 8 名 [0.7%]）、低风险（-LR 0.1 至 0.2；321 名女性 [29.1%]）、中风险（-LR >0.2 和 +LR <5.0；676 名女性 [61.3%]）、高风险（+LR 5.0 至 10.0，87 名女性 [7.9%]）和极高风险（+LR >10.0；11 名女性 [1.0%]）。极低风险组在 48 小时内发生不良母婴事件的比例为 0%，低风险组为 2%，中风险组为 5%，高风险组为 26%，极高风险组为 91%。在外部验证数据集中的 2901 名女性被准确地分为极低风险（0%有结局）、低风险（1%）、中风险（4%）、高风险（33%）或极高风险（67%）。
PIERS-ML 模型提高了识别在评估后 2 天内处于最低和最高严重不良母婴结局风险的子痫前期女性的能力，可支持为女性及其家属、其产科护理提供者提供准确的指导。
斯特拉斯克莱德大学数据链接多样性博士培训中心、胎儿医学基金会、加拿大卫生研究院和比尔及梅林达盖茨基金会。
