Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA; Department of Obstetrics, Gynecology, and Reproductive Biology, Harvard Medical School, Boston, MA.
Am J Obstet Gynecol. 2023 Mar;228(3):338.e1-338.e12. doi: 10.1016/j.ajog.2022.08.045. Epub 2022 Aug 26.
Preeclampsia is a pregnancy complication that contributes substantially to perinatal morbidity and mortality worldwide. Existing approaches to modeling and prediction of preeclampsia typically focus either on predicting preeclampsia risk alone, or on the timing of delivery following a diagnosis of preeclampsia. As such, they are misaligned with typical healthcare interactions during which the 2 events are generally considered simultaneously.
This study aimed to describe the "semicompeting risks" framework as an innovative approach for jointly modeling the risk and timing of preeclampsia and the timing of delivery simultaneously. Through this approach, one can obtain, at any point during the pregnancy, clinically relevant summaries of an individual's predicted outcome trajectories in 4 risk categories: not developing preeclampsia and not having delivered, not developing preeclampsia but having delivered because of other causes, developing preeclampsia but not having delivered, and developing preeclampsia and having delivered.
To illustrate the semicompeting risks methodology, we presented an example analysis of a pregnancy cohort from the electronic health record of an urban, academic medical center in Boston, Massachusetts (n=9161 pregnancies). We fit an illness-death model with proportional-hazards regression specifications describing 3 hazards for timings of preeclampsia, delivery in the absence of preeclampsia, and delivery following preeclampsia diagnosis.
The results indicated nuanced relationships between a variety of risk factors and the timings of preeclampsia diagnosis and delivery, including maternal age, race/ethnicity, parity, body mass index, diabetes mellitus, chronic hypertension, cigarette use, and proteinuria at 20 weeks' gestation. Sample predictions for a diverse set of individuals highlighted differences in projected outcome trajectories with regard to preeclampsia risk and timing, and timing of delivery either before or after preeclampsia diagnosis.
The semicompeting risks framework enables characterization of the joint risk and timing of preeclampsia and delivery, providing enhanced, meaningful information regarding clinical decision-making throughout the pregnancy.
子痫前期是一种妊娠并发症，在全球范围内对围产期发病率和死亡率有重大影响。现有的子痫前期建模和预测方法通常要么专注于单独预测子痫前期的风险，要么专注于子痫前期诊断后的分娩时间。因此，它们与典型的医疗保健互动不一致，在这些互动中，这两个事件通常被同时考虑。
本研究旨在描述“半竞争风险”框架作为一种创新方法，用于同时联合建模子痫前期的风险和时间以及分娩时间。通过这种方法，在妊娠的任何时候，都可以获得个体预测结果轨迹在 4 个风险类别中的临床相关摘要：未发生子痫前期且未分娩、未发生子痫前期但因其他原因分娩、发生子痫前期但未分娩以及发生子痫前期且已分娩。
为了说明半竞争风险方法，我们展示了一个来自马萨诸塞州波士顿一家城市学术医疗中心电子健康记录的妊娠队列的示例分析（n=9161 例妊娠）。我们拟合了一个疾病-死亡模型，使用比例风险回归规范描述了子痫前期、无子痫前期分娩和子痫前期诊断后分娩的 3 个时间风险。
结果表明，各种风险因素与子痫前期诊断和分娩时间之间存在细微的关系，包括母亲年龄、种族/民族、产次、体重指数、糖尿病、慢性高血压、吸烟和 20 周妊娠时的蛋白尿。对一组不同个体的样本预测突出了子痫前期风险和时间以及子痫前期诊断前后分娩时间的预测结果轨迹的差异。
半竞争风险框架能够描述子痫前期和分娩的联合风险和时间，为整个妊娠期间的临床决策提供增强的、有意义的信息。
