Assisted Reproductive Technology as Predictor for Preeclampsia: A Systematic Review of Current Clinical Prediction Models

Reproductive Sciences, vol. 33, pp. 1907–1930

Abstract

Assisted Reproductive Technology (ART) increases the risk of preeclampsia (PE), yet no specific prediction model tailored to the ART population exists. This review evaluates prediction models incorporating ART as a predictor, focusing on risk of bias, performance, generalizability and clinical applicability. A systematic MEDLINE search up to July 2023 identified models predicting PE that included ART as a predictor. Exclusion comprised studies on outcomes after PE onset, univariable models and validation studies of previously validated models. Risk of bias, applicability and generalizability were assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis and Diagnosis (TRIPOD) guideline. Out of 15,305 records, 161 articles were assessed in full-text, yielding fifteen eligible studies. Eleven studies were added from a prior systematic review. Performance varied widely (AUC 0.58-0.99) and only few (7/26) models had been externally validated. Risk of bias was unclear in most studies (21/26), due to analytical issues. Despite ART being an established risk factor for PE, robust and generalizable models with proven external validity are lacking. Future research should prioritize development of a high quality prediction model including ART, or a model developed specifically within ART populations.

Authors 4

1. Géraldine Lafeber corresponding Aachen Department of Obstetrics and Gynaecology

   Leiden University Medical Center

   Affiliation as printed

   Department of Obstetrics and Gynaecology, Leiden University Medical Center, Albinusdreef 2, 2333 ZG, Leiden, the Netherlands. g.c.m.lafeber@lumc.nl

2. Marie‐Louise van der Hoorn Aachen Department of Obstetrics and Gynaecology

   Leiden University Medical Center

   Affiliation as printed

   Department of Obstetrics and Gynaecology, Leiden University Medical Center, Albinusdreef 2, 2333 ZG, Leiden, the Netherlands

3. Saskia le Cessie Aachen Department of Clinical Epidemiology

   Leiden University Medical Center

   Affiliation as printed

   Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZG, Leiden, the Netherlands

4. Lisa Lashley

   Erasmus MC

   Affiliation as printed

   Department of Obstetrics and Gynaecology, Erasmus Medical Center, Molewaterplein 40, 3015 CN, Rotterdam, the Netherlands