First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis

Abstract

Background: Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM). Objectives: To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes. Search Strategy: The PubMed database was searched until 6 June 2024. Selection Criteria: First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded. Data Collection and Analysis: Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST. Main results: A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69–0.78] for PE models, 0.62 [0.60–0.71] for SGA models of nulliparous women, 0.74 [0.72–0.74] for SGA models of multiparous women, 0.65 [0.61–0.67] for sPTB models of nulliparous women, 0.71 [0.68–0.74] for sPTB models of multiparous women and 0.71 [0.67–0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations. Conclusions: Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.