• John Allotey, SnellKym IeKI0000-0001-9373-6591, Melanie Smuk, Richard Hooper, Claire L Chan, Asif Ahmed, Lucy C Chappell, Peter von Dadelszen, Julie Dodds, Marcus Green, Louise Kenny, Asma Khalil, Khalid S Khan, Ben W Mol, Jenny Myers, Lucilla Poston, Basky Thilaganathan, Anne C Staff, Gordon Cs Smith, Wessel Ganzevoort, Hannele Laivuori, Anthony O Odibo, Javier A Ramírez, John Kingdom, George Daskalakis, Diane Farrar, Ahmet A Baschat, Paul T Seed, Federico Prefumo, da Silva CostaFabricioF0000-0002-0765-7780, Henk Groen, Francois Audibert, Jacques Masse, Ragnhild B Skråstad, Kjell Å Salvesen, Camilla Haavaldsen, Chie Nagata, Alice R Rumbold, Seppo Heinonen, Lisa M Askie, Luc Jm Smits, Christina A Vinter, Per M Magnus, Kajantie Eero, Pia M Villa, Anne K Jenum, Louise B Andersen, Jane E Norman, Akihide Ohkuchi, Anne Eskild, Sohinee Bhattacharya, Fionnuala M McAuliffe, Alberto Galindo, Ignacio Herraiz, Lionel Carbillon, Kerstin Klipstein-Grobusch, SeonAe Yeo, Helena J Teede, Joyce L Browne, Karel Gm Moons, Richard D Riley, and Shakila Thangaratinam.
• Health Technol Assess. 2020 Dec 1; 24 (72): 1-252.

BackgroundPre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management.ObjectivesTo assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers.DesignThis was an individual participant data meta-analysis of cohort studies.SettingSource data from secondary and tertiary care.PredictorsWe identified predictors from systematic reviews, and prioritised for importance in an international survey.Primary OutcomesEarly-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia.AnalysisWe externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I 2 and τ2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals.ResultsThe International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia.LimitationsVariations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data.ConclusionFor models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings.Future WorkRecalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.Study RegistrationThis study is registered as PROSPERO CRD42015029349.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

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