Pre-eclampsia (PE) remains a major contributor to maternal morbidity and mortality globally. Early identification of risk factors and evaluation of prognostic models for severe adverse maternal outcomes are essential for improving management and reducing complications. While numerous studies have explored potential risk markers, there is still no consensus on the most reliable factors and models to use in clinical practice. This systematic review aims to consolidate research on both individual predictors and prognostic models of severe adverse maternal outcomes in PE, providing a comprehensive overview to support better clinical decision-making and patient care.

This review follows the Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocol 2015 checklist. A systematic search will be performed using a detailed strategy across Medline, Embase, Cochrane, ProQuest dissertations, and grey literature from inception to 2 April 2024. Eligible studies will include those investigating clinical, laboratory-based, and sociodemographic predictors of severe adverse maternal outcomes in PE. Two reviewers will independently assess titles, abstracts, full texts, and extract data and assess study quality using the Quality In Prognostic Studies (QUIPS) tool for studies on risk predictors and the Prediction model Risk of Bias Assessment Tool (PROBAST) for prognostic models. The inclusion criteria will encompass cohort, case-control, and cross-sectional studies published in English and French involving women diagnosed with PE and reporting on the risk prediction for adverse maternal outcomes. The main outcomes of interest will include severe maternal morbidity and mortality during pregnancy, delivery, or within the postpartum period. Analyses will include both narrative synthesis and, where appropriate, meta-analysis using random-effects models. Pooled estimates will be calculated, with publication bias assessed through funnel plots and statistical tests (eg, Begg’s and Egger’s). Heterogeneity will be primarily assessed through visual inspection of forest plots, supported by statistical measures, such as the I² test, with further exploration through sensitivity, subgroup, and meta-regression analyses.

This systematic review will be based on published data and will not require ethics approval. Results will be disseminated through peer-reviewed publications and presentations at academic conferences.

CRD42024517097.