使用个体参与者数据（IPD）荟萃分析进行子痫前期预测模型的外部验证、更新和开发：国际妊娠并发症预测网络（IPPIC子痫前期）方案
External validation, update and development of prediction models for pre-eclampsia using an Individual Participant Data (IPD) meta-analysis: the International Prediction of Pregnancy Complication Network (IPPIC pre-eclampsia) protocol.
作者信息
Allotey John, Snell Kym I E, Chan Claire, Hooper Richard, Dodds Julie, Rogozinska Ewelina, Khan Khalid S, Poston Lucilla, Kenny Louise, Myers Jenny, Thilaganathan Basky, Chappell Lucy, Mol Ben W, Von Dadelszen Peter, Ahmed Asif, Green Marcus, Poon Liona, Khalil Asma, Moons Karel G M, Riley Richard D, Thangaratinam Shakila
机构信息
1Women's Health Research Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
2Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
出版信息
Diagn Progn Res. 2017 Oct 3;1:16. doi: 10.1186/s41512-017-0016-z. eCollection 2017.
BACKGROUND
Pre-eclampsia, a condition with raised blood pressure and proteinuria is associated with an increased risk of maternal and offspring mortality and morbidity. Early identification of mothers at risk is needed to target management.
METHODS/DESIGN: We aim to systematically review the existing literature to identify prediction models for pre-eclampsia. We have established the International Prediction of Pregnancy Complication Network (IPPIC), made up of 72 researchers from 21 countries who have carried out relevant primary studies or have access to existing registry databases, and collectively possess data from more than two million patients. We will use the individual participant data (IPD) from these studies to externally validate these existing prediction models and summarise model performance across studies using random-effects meta-analysis for any, late (after 34 weeks) and early (before 34 weeks) onset pre-eclampsia. If none of the models perform well, we will recalibrate (update), or develop and validate new prediction models using the IPD. We will assess the differential accuracy of the models in various settings and subgroups according to the risk status. We will also validate or develop prediction models based on clinical characteristics only; clinical and biochemical markers; clinical and ultrasound parameters; and clinical, biochemical and ultrasound tests.
DISCUSSION
Numerous systematic reviews with aggregate data meta-analysis have evaluated various risk factors separately or in combination for predicting pre-eclampsia, but these are affected by many limitations. Our large-scale collaborative IPD approach encourages consensus towards well developed, and validated prognostic models, rather than a number of competing non-validated ones. The large sample size from our IPD will also allow development and validation of multivariable prediction model for the relatively rare outcome of early onset pre-eclampsia.
TRIAL REGISTRATION
The project was registered on Prospero on the 27 November 2015 with ID: CRD42015029349.
背景
子痫前期是一种伴有血压升高和蛋白尿的病症，与孕产妇及子代的死亡和发病风险增加相关。需要尽早识别有风险的母亲以便进行针对性管理。
方法/设计：我们旨在系统回顾现有文献，以确定子痫前期的预测模型。我们建立了国际妊娠并发症预测网络（IPPIC），其由来自21个国家的72名研究人员组成，这些研究人员开展了相关的原始研究或能够访问现有的登记数据库，总共拥有来自超过200万患者的数据。我们将使用这些研究中的个体参与者数据（IPD）对外验证这些现有的预测模型，并使用随机效应荟萃分析总结各项研究中针对任何子痫前期、晚发型（34周后）和早发型（34周前）子痫前期的模型性能。如果没有一个模型表现良好，我们将使用IPD重新校准（更新），或开发并验证新的预测模型。我们将根据风险状态评估模型在不同环境和亚组中的差异准确性。我们还将仅基于临床特征；临床和生化标志物；临床和超声参数；以及临床、生化和超声检查来验证或开发预测模型。
讨论
许多采用汇总数据荟萃分析的系统评价分别或联合评估了各种预测子痫前期的风险因素，但这些都受到许多限制。我们大规模的协作IPD方法有助于就完善且经过验证的预后模型达成共识，而非众多相互竞争的未经验证的模型。我们IPD的大样本量还将允许开发和验证针对早发型子痫前期这一相对罕见结局的多变量预测模型。
试验注册
该项目于2015年11月27日在国际系统评价注册库（Prospero）注册，注册号：CRD42015029349。
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