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External Validation and Clinical Usefulness of First Trimester Prediction Models for the Risk of Preeclampsia: A Prospective Cohort Study

机译:前妊娠期前妊娠期预测模型的外部验证和临床实用性:预期队列研究

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Introduction: This study assessed the external validity of all published first trimester prediction models for the risk of preeclampsia (PE) based on routinely collected maternal predictors. Moreover, the potential utility of the best-performing models in clinical practice was evaluated. Material and Methods: Ten prediction models were systematically selected from the literature. We performed a multicenter prospective cohort study in the Netherlands between July 1, 2013, and December 31, 2015. Eligible pregnant women completed a web-based questionnaire before 16 weeks' gestation. The outcome PE was established using postpartum questionnaires and medical records. Predictive performance of each model was assessed by means of discrimination (c-statistic) and a calibration plot. Clinical usefulness was evaluated by means of decision curve analysis and by calculating the potential impact at different risk thresholds. Results: The validation cohort contained 2,614 women of whom 76 developed PE (2.9%). Five models showed moderate discriminative performance with c-statistics ranging from 0.73 to 0.77. Adequate calibration was obtained after refitting. The best models were clinically useful over a small range of predicted probabilities. Discussion: Five of the ten included first trimester prediction models for PE showed moderate predictive performance. The best models may provide more benefit compared to risk selection as used in current guidelines.
机译:介绍:本研究评估了所有已发表的前妊娠前期预测模型的外部有效性,用于基于常规收集的母体预测因子的预普拉帕西亚(PE)的风险。此外,评估了临床实践中最佳性能模型的潜在效用。材料和方法:从文献系统地选择10个预测模型。我们在2013年7月1日和2015年12月31日在荷兰进行了多中心预期队列研究。符合条件的孕妇在16周之前完成了基于网络的问卷。使用产后问卷和医疗记录建立了结果PE。通过歧视(C统计)和校准图来评估每个模型的预测性能。通过决策曲线分析评估临床有用性,并通过计算不同风险阈值的潜在影响。结果:验证队列含有2,614名妇女,其中76名PE(2.9%)。五种模型表现出适度的鉴别性能,C统计量范围为0.73至0.77。重新安装后获得了足够的校准。最好的模型在临床上是有用的,在少量预测概率上有用。讨论:十种中的五个包括PE的第一个妊娠期预测模型显示出中等的预测性能。与当前指南中使用的风险选择相比,最好的模型可以提供更多的好处。

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