Construction and Evaluation of a Predictive Nomogram for Identifying Premature Failure of Arteriovenous Fistulas in Elderly Diabetic Patients

Background: This research aimed to identify risk factors contributing to premature maturation of arteriovenous fistulas (AVF) in elderly diabetic patients and develop a clinical prediction model.

Methods: We conducted a retrospective review of 548 geriatric diabetic patients who underwent AVF creation for maintenance hemodialysis (MHD) at Baoding No 1 Central Hospital between January 2011 and December 2023. Patients were divided into mature (386) and immature (162) groups based on AVF maturation status. Univariate logistic regression analysis and the least absolute shrinkage and selection operator were used to identify independent risk factors, including D-dimer levels, low-density lipoprotein cholesterol levels, internal radial meridian, radial artery plaque presence, and cephalic vein indwelling needle use history. A predictive nomogram was developed specifically for immature AVF in elderly diabetic patients. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results: Among elderly patients with diabetes mellitus, the incidence of immature AVF was 29.56%, affecting 162 of 548 individuals. The five-variable model demonstrated an AUROC value of 0.922, with a 95% confidence interval (CI) of 0.870 to 0.947 in the training dataset, and an AUROC of 0.912, accompanied by a 95% CI of 0.880 to 0.935 in the internal validation dataset. The calibration curve, derived from 1000 bootstrap samples, showed good agreement between predicted and observed outcomes. Additionally, both the DCA and CIC exhibited favorable clinical utility and net benefits.

Conclusions: The nomogram prediction model, based on independent risk factors, serves as a valuable tool for accurate prognosis and has potential to aid in establishing and preserving hemodialysis access in elderly diabetic patients, ultimately optimizing their healthcare outcomes.