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A Novel Murine Model of Arteriovenous Fistula Failure: The Surgical Procedure in Detail
Published on: February 3, 2016
Risk prediction models for autogenous arteriovenous fistula failure in maintenance hemodialysis patients: A
Minghua Han1, Qian Zhao2, Jian Zhao1
1School of Nursing, Shanxi Medical University, Taiyuan, Shanxi, China.
Background:
The aim of this study was to systematically retrieve and evaluate published risk prediction models for autogenous arteriovenous fistula (AVF) failure post-formation in maintenance hemodialysis (MHD) patients, with the goal of assisting healthcare providers in selecting or developing appropriate risk assessment tools and providing a reference for future research.
Methods:
A systematic search of relevant studies was conducted in PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang Database, VIP Database, and CBM Database up to February 1, 2024. Two researchers independently performed literature screening, data extraction, and methodological quality assessment using the Prediction Model Risk of bias (ROB) Assessment Tool.
Results:
A total of 4869 studies were identified, from which 25 studies with 28 prediction models were ultimately included. The incidence of autogenous AVF failure in MHD patients ranged from 3.9% to 39%. The most commonly used predictors were age, vein diameter, history of diabetes, AVF blood flow, and sex. The reported area under the curve (AUC) ranged from 0.61 to 0.911. All studies were found to have a high ROB, primarily due to inappropriate data sources and a lack of rigorous reporting in the analysis domain. The pooled AUC of five validation models was 0.80 (95% confidence interval: 0.79-0.81), indicating good predictive accuracy.
Conclusion:
The included studies indicated that the predictive models for AVF failure post-formation in MHD patients are biased to some extent. Future research should focus on developing new models with larger sample sizes, strict adherence to reporting procedures, and external validation across multiple centers.
