# An interpretable nomogram with SHAP analysis predicts thrombotic failure of forearm arteriovenous fistulas

## TL;DR

A new tool using a nomogram and SHAP analysis helps predict when blood vessels used for dialysis might fail due to blood clots.

## Contribution

The study introduces a novel interpretable nomogram combined with SHAP analysis for predicting AVF thrombosis in hemodialysis patients.

## Key findings

- The nomogram identified hypertension, hypotension, BMI, cholesterol, CRP, and parathyroid hormone as independent predictors of AVF thrombosis.
- The model showed good discrimination with AUCs of 0.80 in training and 0.71 in validation sets.
- SHAP analysis highlighted red cell distribution width-standard deviation as the most influential predictor.

## Abstract

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.

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.

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.

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.