Development and validation of a risk prediction model for autologous arteriovenous fistula thrombosis in patients receiving maintenance hemodialysis

BackgroundThrombosis can lead to fistula failure and affect the smooth progress of hemodialysis. This study aims to develop and validate a nomogram for predicting the risk of autologous arteriovenous fistula thrombosis in patients undergoing maintenance hemodialysis.MethodsA total of 1,016 patients who underwent hemodialysis at a tertiary A hospital in East China from February 2020 to March 2024 were retrospectively enrolled. The participants were randomly divided into a training set (711 people) and a validation set (305 people) at a ratio of 7:3. A risk prediction model was established according to the results of multivariate logistic regression analysis. The performance of the model was evaluated with the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve analysis, the Hosmer-Lemeshow (H-L) test and decision curve analysis (DCA).ResultsThe incidence of autologous arteriovenous fistula thrombosis in patients on maintenance hemodialysis was 32%. High-sensitivity C-reactive protein (hs-CRP), catheterization history, hemodialysis duration, autologous arteriovenous fistula stenosis and non-high-density lipoprotein cholesterol (non-HDL-C) were independent risk factors for autologous arteriovenous fistula thrombosis. These five predictors were used to construct a predictive nomogram. The AUC was 0.818 in the training set and 0.826 in the validation set. The calibration curve of the nomogram was close to the standard curve, indicating that the model was well calibrated. The DCA results confirmed that the model provided good net clinical benefits.ConclusionIn this study, a predictive nomogram for arteriovenous fistula thrombosis was established and validated.