Risk prediction models for pregnancy outcomes in recurrent pregnancy loss: a narrative systematic review.

OBJECTIVE

Recurrent pregnancy loss (RPL) is a significant clinical challenge, with many cases remaining unexplained, and existing risk prediction models often lacking objective evaluation. This study aims to systematically review and evaluate the published risk prediction models for pregnancy outcomes in RPL.

METHODS

Literature search was conducted in August 2024 using PubMed, Embase, Web of Science, CNKI, and CMAJ databases to identify studies that reported the development and/or validation of clinical prediction models for RPL pregnancy outcomes. Pregnancy outcomes included pregnancy loss, ongoing pregnancy, and live birth. Data were extracted using the CHARMS checklist. Risk of bias and applicability were evaluated with PROBAST.

RESULTS

A total of 1,112 records were identified, with 15 studies ultimately included, encompassing 22 risk prediction models for evaluating RPL patients' pregnancy outcomes. The majority were retrospective cohort studies (13/15), and logistic regression was the predominant modeling method (14/15). Sample sizes ranged from 85 to 789, with the number of predictors per model varying from 2 to 18 (median=5). In total, 65 distinct predictors were identified, including five categories: patient-related, imaging-related, thrombophilia-related, metabolic/endocrinologic, and immunological factors, most frequently maternal age (n=10) and number of previous pregnancy losses (n=9). Among the 20 models that reported discriminative performance by the area under the receiver operating characteristics (ROC) curve (AUC), 13 achieved AUC above 0.80 (range: 0.809-0.97). Notably, 7 studies did not perform any form of validation, and only 3 studies conducted external validation. Despite the models reported a good predictive performance, they were all appraised to have high risk of bias in applicability due to methodological deficiencies.

CONCLUSION

The findings suggest that current risk prediction models for RPL pregnancy outcomes have a high risk of bias in clinical applications, primarily due to methodological flaws in development and validation processes. Future research should focus on data quality, sample diversity, and model transparency to ensure broad applicability across different populations, providing more reliable and effective tools for clinical practice.

SYSTEMATIC REVIEW EEGISTRATION

https://www.crd.york.ac.uk/PROSPERO/view/CRD42024570481, identifier CRD42024570481.