Risk prediction models for peristomal moisture-associated skin damage in China: a systematic review and meta-analysis

Background: Peristomal Moisture-associated Skin Damage (PMASD) is a complication of enterostomy that significantly increases the healthcare burden and contributes to poorer patient prognoses. Risk prediction models for PMASD offer significant utility in directing prophylactic strategies. However, the methodological quality and applicability of existing models remain uncertain. Objective: This study aims to systematically identify and critically evaluate currently available risk prediction models for PMASD. Methods: PubMed, the Cochrane Library, Embase, Web of Science, CINAHL, Scopus, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Wanfang Database, and Chinese Biomedical literature Database (CBM) were systematically searched from inception to 1 January 2026. Two researchers independently screened the literature and extracted and evaluated information based on the Prediction Model Risk of Bias Assessment Tool (PROBAST) and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). R4.4.0 software was used to conduct meta-analysis. Results: A total of 11 prediction models from 10 studies were included, with an incidence rate ranging from 22.8 to 59.1%. All studies indicated a substantial risk of bias, thus limiting their utility in clinical practice. The area under the curve (AUC) values of 11 models ranged from 0.812 to 0.914. The history of radiotherapy, type of stoma, stoma opening height, and surgical wound in the plate area were identified as the strongest predictors. In total, three studies validated the model externally, and six studies validated the model through an integration of internal and external methods, whereas one study did not undergo any validation after model development. Conclusion: In this systematic review, although most models performed well in terms of applicability, all models exhibited inherent limitations due to a high risk of bias. In the future, large-sample, multicenter, and high-quality prospective clinical studies should be carried out to optimize the predictive models, so as to improve their predictive ability and clinical application value. Systematic trial registration: identifier: CRD420251089071.