# Predicting outcomes following endovascular aortoiliac revascularization using machine learning

## TL;DR

This study uses machine learning to predict 30-day outcomes after a common blood vessel procedure, showing better accuracy than traditional methods.

## Contribution

A novel machine learning model (XGBoost) is developed to predict post-procedural outcomes after endovascular revascularization, outperforming logistic regression.

## Key findings

- XGBoost achieved an AUROC of 0.94 for predicting 30-day MALE/death outcomes.
- Logistic regression had a lower AUROC of 0.74 for the same outcome.
- The model was trained on 37 pre-operative variables from 6,601 patients.

## Abstract

Endovascular aortoiliac revascularization is a common treatment option for peripheral artery disease that carries non-negligible risks. Outcome prediction tools may support clinical decision-making but remain limited. We developed machine learning algorithms that predict 30-day post-procedural outcomes. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent endovascular aortoiliac revascularization between 2011–2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day post-procedural major adverse limb event (MALE) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using pre-operative features. Overall, 6601 patients were included, and 30-day MALE/death occurred in 470 (7.1%) individuals. The best-performing model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93–0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.74 (0.73–0.76). The XGBoost model accurately predicted 30-day post-procedural outcomes, performing better than logistic regression.