Preeclampsia risk prediction model for Chinese pregnant women (ChiPERM): research protocol for a randomized stepped-wedge cluster trial

Abstract

BackgroundDespite international clinical guideline recommendations, implementation of Bayes-theorem based preeclampsia risk prediction model in first trimester among Chinese women is limited. The aim of this study is to examine the effectiveness of this risk predictive strategy in reducing the risk of preeclampsia.MethodsThe study will be a randomized, stepped-wedge controlled trial conducted in eighteen hospitals in China. Stepped implementation of Bayes-theorem based risk prediction model will be delivered to hospitals in a random order to support the introduction of this prediction model of preeclampsia. A staged process will be undertaken to develop the risk prediction strategies, which comprise of: combined risk evaluation by maternal risk factors, medium arterial pressure, uterine artery pulse index and placenta growth factor during 11–13+6 gestational weeks, monthly follow up (including blood pressure, newly onset complications, adherence to aspirin). Repeated cross-sectional outcome data will be gathered weekly across all hospitals for the study duration. The primary outcome measures are the incidence of preeclampsia within 42 days postpartum. Data on resources expended during intervention development and implementation will be collected. The incidence of pregnancy related complications will be measured as secondary outcomes.DiscussionThis will be the first randomized controlled trial to evaluate the effectiveness of the Bayes-theorem based preeclampsia risk prediction strategies in first trimester by competing risk model validation. If positive changes in clinical practice are found, this evidence will support health service adoption of this risk prediction model to reduce the risk of preeclampsia among Chinese pregnant women.Trial registrationChinese Clinical Trials Registry, No. ChiCTR2100043520 (date registered:21/2/2021).