1. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis
- Author
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Allotey, J. Snell, K.I.E. Smuk, M. Hooper, R. Chan, C.L. Ahmed, A. Chappell, L.C. von Dadelszen, P. Dodds, J. Green, M. Kenny, L. Khalil, A. Khan, K.S. Mol, B.W. Myers, J. Poston, L. Thilaganathan, B. Staff, A.C. Smith, G.C.S. Ganzevoort, W. Laivuori, H. Odibo, A.O. Ramírez, J.A. Kingdom, J. Daskalakis, G. Farrar, D. Baschat, A.A. Seed, P.T. Prefumo, F. da Silva Costa, F. Groen, H. Audibert, F. Massé, J. Skråstad, R.B. Salvesen, K.A. Haavaldsen, C. Nagata, C. Rumbold, A.R. Heinonen, S. Askie, L.M. Smits, L.J.M. Vinter, C.A. Magnus, P.M. Eero, K. Villa, P.M. Jenum, A.K. Andersen, L.B. Norman, J.E. Ohkuchi, A. Eskild, A. Bhattacharya, S. McAuliffe, F.M. Galindo, A. Herraiz, I. Carbillon, L. Klipstein-Grobusch, K. Yeo, S. Teede, H.J. Browne, J.L. Moons, K.G.M. Riley, R.D. Thangaratinam, S. The IPPIC Collaborative Network and Allotey, J. Snell, K.I.E. Smuk, M. Hooper, R. Chan, C.L. Ahmed, A. Chappell, L.C. von Dadelszen, P. Dodds, J. Green, M. Kenny, L. Khalil, A. Khan, K.S. Mol, B.W. Myers, J. Poston, L. Thilaganathan, B. Staff, A.C. Smith, G.C.S. Ganzevoort, W. Laivuori, H. Odibo, A.O. Ramírez, J.A. Kingdom, J. Daskalakis, G. Farrar, D. Baschat, A.A. Seed, P.T. Prefumo, F. da Silva Costa, F. Groen, H. Audibert, F. Massé, J. Skråstad, R.B. Salvesen, K.A. Haavaldsen, C. Nagata, C. Rumbold, A.R. Heinonen, S. Askie, L.M. Smits, L.J.M. Vinter, C.A. Magnus, P.M. Eero, K. Villa, P.M. Jenum, A.K. Andersen, L.B. Norman, J.E. Ohkuchi, A. Eskild, A. Bhattacharya, S. McAuliffe, F.M. Galindo, A. Herraiz, I. Carbillon, L. Klipstein-Grobusch, K. Yeo, S. Teede, H.J. Browne, J.L. Moons, K.G.M. Riley, R.D. Thangaratinam, S. The IPPIC Collaborative Network
- Abstract
Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objectives: To 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. Design: This was an individual participant data meta-analysis of cohort studies. Setting: Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes: Early-onset (delivery at < 34 weeks’ gestation), late-onset (delivery at ≥ 34 weeks’ gestation) and any-onset pre-eclampsia. Analysis: We 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 I2 and 2. 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. Results: The 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 v
- Published
- 2020