# Predictors of Mortality in Peripheral Arterial Disease After Endovascular Lower Limb Revascularization and Development of a Risk Score Based Solely on Clinical Presentation

## TL;DR

This study identifies clinical factors that predict long-term mortality in PAD patients after revascularization and creates a risk score based on these factors.

## Contribution

A new clinical risk score (GZ-PAD Mortality Score) is developed for mortality prediction in PAD patients using only clinical factors.

## Key findings

- Age, coronary artery disease, chronic kidney disease, dialysis dependence, and tissue loss were independent predictors of mortality.
- The GZ-PAD Mortality Score effectively stratified patients into low, moderate, and high-risk groups with significant survival differences.
- The risk score showed good discriminative performance with an AUC of 0.769.

## Abstract

Background: Patients with peripheral arterial disease (PAD) undergoing endovascular revascularization remain at high risk of long-term mortality. While anatomical characteristics such as the length of the lesion, chronic total occlusions and multi-segmental distribution strongly influence revascularization strategy and limb outcomes, their prognostic impact on survival is less clearly defined. The combination of clinical comorbidities, clinical limb presentation and anatomical factors may help to better predict mortality rate before endovascular lower limb revascularization. The primary endpoint of this study was to identify independent predictors of mortality in PAD patients, and the secondary endpoint was to develop a simple clinical risk score for individualized prognostic stratification. Methods: We conducted a single-center retrospective observational study including 476 consecutive PAD patients undergoing endovascular revascularization over a 6-year period. The endpoint target considered was all-cause mortality. Cox proportional hazards regression was used to identify independent predictors of mortality. A prognostic model was derived and subsequently simplified into a point-based clinical risk score (GZ-PAD Mortality Score). Model performance was assessed within the study cohort using Kaplan–Meier survival stratification and receiver operating characteristic (ROC) analysis. Results: Multivariable analysis identified age (HR 1.041 per year, p < 0.001), coronary artery disease (CAD) (HR 1.56, p < 0.001), chronic kidney disease (CKD) (HR 1.52, p = 0.038), dialysis dependence (HR 2.50, p < 0.001), and tissue loss (Rutherford 5–6; HR 5.33, p < 0.001) as independent predictors of mortality, whereas anatomical variables such as lesion length, chronic total occlusions and poor run-off vessel lost prognostic significance. For each patient, the linear predictor (XBETA) was calculated from the coefficients of the final Cox regression model and used to build the mortality score. Based on the 33rd and 66th percentiles of the XBETA distribution, patients were stratified into three prognostic categories: low risk (XBETA ≤ −0.43081), moderate risk (−0.43080 to 0.50835), and high risk (>0.50836). Kaplan–Meier analysis showed a significant discrimination (log-rank χ2 = 102.441; p < 0.001) and good discriminative performance (AUC 0.752). Next the model based on the XBETA predictor was simplified into the global ischemic-systemic risk score (GZ-PAD) assigned for ages 60–69 years = 1 point; 70–79 years = 2 points; ≥80 years = 3 points, CAD = 2 points; CKD = 1 point; Dialysis dependence = 3 points; and tissue loss = 6 points. The new model assessed with survival curves provided robust risk stratification across low-, moderate-, and high-risk groups (log-rank χ2 = 88.883; p < 0.001) and preserved predictive accuracy (AUC 0.769). Conclusions: In PAD patients undergoing successful endovascular revascularization, long-term survival appeared to be related to systemic clinical factors and ischemic severity rather than anatomical lesion complexity. The GZ-PAD Mortality Score offers a simple and clinically applicable tool for mortality risk stratification. Further studies, including external validation in independent and multicenter cohorts, are needed to confirm the robustness and generalizability of the proposed risk score.