Department of Obstetrics and Gynecology, Christiana Care Health System, Newark, DE.
Cerner Intelligence, Cerner Corporation, Kansas City, MO.
Am J Obstet Gynecol MFM. 2021 Jan;3(1):100250. doi: 10.1016/j.ajogmf.2020.100250. Epub 2020 Oct 6.
Maternal postpartum hypertensive emergency is a major cause of maternal mortality and maternal readmission, yet prediction of women who require readmission is limited with false negatives and false positives.
This study aimed to develop and validate a predictive algorithm for maternal postpartum readmission from complications of hypertensive disorders of pregnancy using machine learning.
We performed a cohort study of pregnant women delivering at a single institution using prospectively collected clinical information available from the electronic medical record at the time of discharge. Our primary outcome was readmission within 42 days of delivery for complications of hypertensive disorders of pregnancy. The data set was divided into a derivation and a separate validation cohort. In the derivation cohort, 10 independent data sets were created by randomly suppressing 10% of the population, and then clinical features predictive of complications of hypertensive disorders of pregnancy were analyzed using machine learning to optimize the area under the curve. Once an optimal model was determined, this model was then validated using a second independent validation cohort.
A total of 20,032 delivering women with 238 readmissions for complications of hypertensive disorders of pregnancy (1.2%) were included in the derivation cohort. The validation cohort consisted of 5823 women with 82 readmissions for complications of hypertensive disorders of pregnancy (1.4%). The demographics were similar between the 2 populations as was the test performance characteristics (area under the curve, 0.85 in the derivation cohort vs 0.81 in the validation cohort). Both the derivation and validation algorithms used 31 clinical features that were found to be comparably predictive in both models.
In this evaluation of a machine learning algorithm, readmission for complications of hypertensive disorders of pregnancy can be predicted with reasonable accuracy using clinical data at the time of discharge. Validation of this model in other care settings is necessary to validate its utility.
产后高血压急症是导致产妇死亡和再次入院的主要原因，但目前预测需要再次入院的女性的方法存在假阴性和假阳性，预测效果有限。
本研究旨在使用机器学习开发和验证一种预测与妊娠高血压疾病相关并发症的产后再次入院的预测算法。
我们对在一家机构分娩的孕妇进行了队列研究，使用了在出院时从电子病历中获得的前瞻性收集的临床信息。我们的主要结局是产后 42 天内因妊娠高血压疾病并发症而再次入院。数据集分为推导和独立验证队列。在推导队列中，通过随机抑制 10%的人群创建了 10 个独立数据集，然后使用机器学习分析预测妊娠高血压疾病并发症的临床特征，以优化曲线下面积。一旦确定了最佳模型，就使用第二个独立验证队列对该模型进行验证。
共有 20032 名分娩妇女，其中 238 人因妊娠高血压疾病并发症而再次入院（1.2%），被纳入推导队列。验证队列由 5823 名妇女组成，其中 82 人因妊娠高血压疾病并发症而再次入院（1.4%）。两个队列之间的人口统计学特征相似，测试性能特征也相似（曲线下面积，推导队列为 0.85，验证队列为 0.81）。两个队列的算法都使用了 31 个临床特征，这些特征在两个模型中都具有相似的预测能力。
在这项对机器学习算法的评估中，使用出院时的临床数据可以合理准确地预测妊娠高血压疾病并发症的再次入院。需要在其他护理环境中验证该模型，以验证其有效性。
