Machine Learning Model of Clinical Risk Factors and Peripheral Calcium Scoring on Computed Tomography for the Prediction of Amputation in Patients with Peripheral Arterial Disease

Abstract

To date, there is no quantifiable evidence of the preferred measure of peripheral arterial disease (PAD) to best predict outcomes. Lower extremity calcium scoring is a novel tool that helps risk stratify patients with PAD. This study uses a machine learning (ML)-based approach to develop risk scores based on clinical variables and peripheral calcium scoring in an attempt to predict risk of major amputation in patients with PAD.