External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis.
External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis.
复制标题
使用国际妊娠并发症预测 (IPPIC) 网络数据库对预测死产的预后模型进行外部验证：个体参与者数据荟萃分析。
DOI：
10.1002/uog.23757
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Allotey J
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作者：
Allotey J
死胎是一种潜在的可预防的妊娠并发症。确定死产高风险妇女可以指导决定是否需要更密切的监测和分娩时间，以防止胎儿死亡。已经开发了预测模型来预测死产的风险，但尚未得到外部验证。在这项研究中，我们使用个体参与者数据（IPD）Meta分析外部验证了已发表的死胎预测模型，以评估其预测性能。方法MEDLINE，EMBASE，DH‐DATA和AMED数据库从开始到2020年12月进行检索，以确定报告死胎预测模型的研究。纳入了开发或更新死产预测模型以供妊娠期间任何时间使用的研究。来自国际妊娠并发症预测网络（IPPIC）内队列的IPD用于外部验证所识别的预测模型，其个体变量在IPD中可用。使用预测研究偏倚风险评估工具（PROBAST）评估模型和队列的偏倚风险。使用C统计量评价模型的区分性能，并使用校准图、校准斜率和大校准评估校准。在每个队列中单独估计性能指标，并使用随机效应Meta分析对队列进行总结。临床效用进行了评估，使用净benefit.Results17项研究报告40死产的预后模型的发展。这些模型之前都没有经过外部验证，只有五分之一（20%，8/40）的模型报告了完整的模型方程。外部验证这些模型中的三个是可能的，使用IPPIC网络数据库中19个队列（491201名孕妇）的IPD。根据对模型开发研究的评估，所有三种模型都具有总体高偏倚风险。在IPD Meta分析中，模型的汇总C统计量范围为0.53至0.65，汇总校准斜率范围为0.40至0.88，与观察到的风险相比，风险预测通常过于极端。根据净效益评估，这些模型几乎没有临床实用性。然而，仍然存在不确定性，在一些模型的性能，由于小的可用样本sizes.ConclusionsThe三个验证死胎预测模型普遍表现出较差的和不确定的预测性能，在新的数据，有限的证据来支持其临床应用。研究结果表明，在他们的发展，包括过度拟合方法上的缺陷。需要进一步的研究来进一步验证这些模型和其他模型，确定更强的预后因素，并开发更可靠的预测模型。© 2021作者由John Wiley & Sons Ltd代表国际妇产科超声学会出版的《妇产科超声》。
ObjectiveStillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta‐analysis to assess their predictive performance.MethodsMEDLINE, EMBASE, DH‐DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using theC‐statistic, and calibration was assessed using calibration plots, calibration slope and calibration‐in‐the‐large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random‐effects meta‐analysis. Clinical utility was assessed using net benefit.ResultsSeventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one‐fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta‐analysis, the models had summaryC‐statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes.ConclusionsThe three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.