ORIGINAL RESEARCH article
Front. Surg.
Sec. Vascular Surgery
This article is part of the Research TopicThe Use of Artificial Intelligence for Diagnostics and Treatment in Vascular SurgeryView all 4 articles
An Interpretable Nomogram with SHAP Analysis Predicts Thrombotic Failure of Forearm Arteriovenous Fistulas
Provisionally accepted
- Second Affiliated Hospital of Soochow University, Suzhou, China
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Objective: End-stage renal disease is an increasing global health problem. Arteriovenous fistula (AVF) thrombosis is a major cause of access failure in maintenance hemodialysis (MHD) patients. An interpretable nomogram, integrated with SHapley Additive exPlanations (SHAP) analysis is developed and validated for predicting thrombotic failure of forearm AVFs in MHD patients. Methods: A single-center retrospective cohort study enrolled 302 MHD patients with dysfunctional forearm AVFs undergoing percutaneous transluminal angioplasty. Patients were randomly allocated to training (70%) and validation (30%) sets. Univariable and multivariable logistic regression identified independent predictors for AVF thrombosis. A nomogram was constructed and its performance evaluated by the area under the receiver operating characteristic curve, calibration, and decision curve analysis. SHAP analysis was applied to quantify feature importance and directionality in the validation set. Results: The final model identified hypertension history, frequent intradialytic hypotension, body mass index, total cholesterol, C-reactive protein, and intact parathyroid hormone as independent predictors. The nomogram demonstrated good discrimination, with AUCs of 0.80 (95% CI: 0.73–0.86) in the training set and 0.71 (95% CI: 0.59–0.83) in the validation set, along with satisfactory calibration and clinical utility. SHAP analysis revealed red cell distribution width-standard deviation as the most influential predictor for individual risk, highlighting a distinction between statistical significance and predictive contribution. Conclusion: This study presents an interpretable nomogram with robust performance for predicting AVF thrombosis. The integration of SHAP analysis enhances model transparency and clinical trust, providing a valuable tool for personalized risk assessment and potential targeting of preventive strategies in MHD patients. Further external validation is warranted.
Keywords: Arteriovenous Shunt, Surgical, Explainable artificial intelligence, Nomograms, Renal Dialysis, Thrombosis
Received: 18 Dec 2025; Accepted: 03 Feb 2026.
Copyright: © 2026 Xu, Jiang, Zhang, Ni, Qian, Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhi Wang
Weiwei Li
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