Risk prediction models for cardiovascular events in hemodialysis patients: A systematic review

Abstract

AbstractObjectiveTo perform a systematic review of risk prediction models for cardiovascular (CV) events in hemodialysis (HD) patients, and provide a reference for the application and optimization of related prediction models.MethodsPubMed, The Cochrane Library, Web of Science, and Embase databases were searched from inception to 1 February 2023. Two authors independently conducted the literature search, selection, and screening. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to evaluate the risk of bias and applicability of the included literature.ResultsA total of nine studies containing 12 models were included, with performance measured by the area under the receiver operating characteristic curve (AUC) lying between 0.70 and 0.88. Age, diabetes mellitus (DM), C‐reactive protein (CRP), and albumin (ALB) were the most commonly identified predictors of CV events in HD patients. While the included models demonstrated good applicability, there were still certain risks of bias, primarily related to inadequate handling of missing data and transformation of continuous variables, as well as a lack of model performance validation.ConclusionThe included models showed good overall predictive performance and can assist healthcare professionals in the early identification of high‐risk individuals for CV events in HD patients. In the future, the modeling methods should be improved, or the existing models should undergo external validation to provide better guidance for clinical practice.