Predicting the Severity of Infrapopliteal Artery Lesions in Patients With Peripheral Artery Disease Using Interpretable Machine Learning.

The GBM model, developed using 6 clinically relevant variables, enables accurate prediction of infrapopliteal artery disease severity and demonstrates its significant potential to support clinical decision-making and improve risk stratification for patients with PAD.Clinical ImpactThis study introduces a Gradient Boosting Machine model to predict the severity of infrapopliteal arterial disease using six readily available variables. For clinicians, this offers a non-invasive, rapid decision-support tool. By accurately predicting lesion severity, the model enables surgeons to better stratify patients, optimize treatment planning, and potentially improve the suboptimal outcomes currently associated with infrapopliteal interventions. This innovation shifts practice towards a more personalized, data-driven approach in the initial assessment of peripheral artery disease.