Systematic Review and External Validation of Clinical Prediction Models for Adverse Pregnancy Outcomes Using Routinely Collected Pre-Conception and Early Pregnancy Data

Objective

Although numerous clinical prediction models (CPMs) have been developed to identify pregnancies at risk of serious adverse outcomes, most lack robust external validation, which restricts their reliability in clinical practice. This study aims to systematically review and externally validate existing clinical prediction models for Gestational Diabetes Mellitus (GDM), Pre-eclampsia (PE), Stillbirth, and Small-for-Gestational-Age (SGA) focusing on models for use in pre-conception or early pregnancy. The models were evaluated using a large, representative primary care cohort to assess their performance within a UK population. Method and Analysis: A two-stage systematic review identified CPMs for GDM, PE, Stillbirth, and SGA. Eligible models used routinely collected maternal characteristics, fully reported the model equation, and included predictors commonly measured during the preconception or early pregnancy period. Validation was performed using the UK Clinical Practice Research Datalink (CPRD Aurum), including over 1.58 million pregnancies for women aged 14 - 49 between 2000 to 2020. Model performance was assessed through discrimination (C-statistic), calibration-in-the-large (CITL), calibration slope and calibration plot, with pooled estimates across 20 imputations.