Risk Prediction of Arteriovenous Fistula Dysfunction in Hemodialysis Patients Using Routine Clinical Indicators.

INTRODUCTION

Arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis (HD) patients, yet AVF dysfunction remains a prevalent complication in maintenance HD. The risk factors influencing AVF patency are not fully defined. This study aimed to identify key clinical predictors and develop a practical model for predicting AVF dysfunction in HD patients.

METHODS

We retrospectively reviewed medical records of HD patients treated between January 1, 2020, and February 28, 2025, at the Hemodialysis Center of the People's Hospital of Baoan, Shenzhen. Demographic characteristics, history of cardiometabolic disease, and laboratory parameters were evaluated. A Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to select the most relevant predictors, followed by multivariate Cox proportional hazards regression to construct the final prediction model. Model discrimination was assessed using the concordance index (C-index), and internal validation was performed via bootstrap resampling.

RESULTS

Among 439 patients (median age 53 years; 61.3% male), 46 (10.5%) developed AVF dysfunction over a median follow-up of 2.9 years. LASSO regression identified five variables - total protein, albumin, left ventricular ejection fraction (LVEF), history of hypertension, and history of heart disease - as the most predictive. In the multivariate Cox model, all five variables remained statistically significant: total protein (hazard ratio [HR]: 0.604; 95% confidence interval [CI]: 0.372-0.983), albumin (HR: 0.468; 95% CI: 0.225-0.969), LVEF (HR: 0.627; 95% CI: 0.522-0.753), history of hypertension (HR: 2.234; 95% CI: 1.086-4.598), and history of heart disease (HR: 1.950; 95% CI: 1.024-3.715). The final model yielded a C-index of 0.812 (95% CI: 0.753-0.871), with consistent performance in internal bootstrap validation.

CONCLUSION

This study identified five routinely available clinical variables as independent predictors of AVF dysfunction in HD patients and developed a nomogram with strong predictive accuracy. This tool may support early risk stratification and guide timely interventions to reduce AVF failure and improve dialysis efficacy.