Key result
Risk prediction models for cardiovascular events in hemodialysis patients demonstrated good predictive performance, with AUC values ranging between 0.70 and 0.88.
Why the study?
To provide a reference for the application and optimization of risk prediction models for cardiovascular events in hemodialysis patients.
What is the predictive performance of risk prediction models for cardiovascular events in hemodialysis patients?
Systematic Review
What is the predictive performance of risk prediction models for cardiovascular events in hemodialysis patients?
Existing risk prediction models for cardiovascular events in hemodialysis patients show good overall predictive performance (AUC 0.70-0.88) but require improved modeling methods and external validation.
Existing CV risk models in HD warrant cautious application; leaves open the need for optimized, low-bias predictors.
OBJECTIVE: To 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. METHODS: PubMed, 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. RESULTS: A 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. CONCLUSION: The 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.
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Gan et al. (2023) conducted a systematic review in Cardiovascular events in hemodialysis patients. Risk prediction models was evaluated on Model performance measured by the area under the receiver operating characteristic curve (AUC) (AUC 0.70 to 0.88). Risk prediction models for cardiovascular events in hemodialysis patients demonstrated good predictive performance, with AUC values ranging between 0.70 and 0.88.
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