Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study
Authors: Thangaratinam Shakila , Allotey John , Marlin Nadine , Mol Ben W , Von Dadelszen Peter , Ganzevoort Wessel , Akkermans Joost , Ahmed Asif , Daniels Jane , Deeks Jon , Ismail Khaled , Barnard Ann Marie , Dodds Julie , Kerry Sally , Moons Carl , Riley Richard D , Khan Khalid S 
Published: April 2017

BackgroundThe prognosis of early-onset pre-eclampsia (before 34 weeks’ gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women.ObjectiveTo develop and validate prediction models for outcomes in early-onset pre-eclampsia.DesignProspective cohort for model development, with validation in two external data sets.SettingModel development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies.ParticipantsPregnant women with early-onset pre-eclampsia.Sample sizeNine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets.PredictorsThe predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey.Main outcome measuresThe primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications.AnalysisWe developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope).ResultsThe PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with anoptimism-adjustedc-statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with ac-statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had ac-statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications.ConclusionsThe PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high- or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation.Trial registrationCurrent Controlled Trials ISRCTN40384046.FundingThe National Institute for Health Research Health Technology Assessment programme.