Prediction of Peripheral Artery Disease Treatment Decisions and Outcomes Using Machine Learning and Gait-Derived Digital Biomarkers
Background: Peripheral artery disease (PAD) is clinically heterogeneous, complicating identification, treatment selection, and prediction of therapeutic benefit. Gait biomechanics provide high-resolution, objective digital biomarkers that can be used in routine care. Methods: We built a computational framework for three tasks: (1) distinguish controls versus PAD; (2) predict clinician-selected treatment (nonoperative vs. revascularization); and (3) forecast post-treatment ground-reaction force (GRF) from baseline GRF features. The dataset included 55 unique patients with PAD and 42 controls. Standard classifiers and regressors were evaluated, and performance was measured by accuracy, balanced accuracy, Matthew’s correlation coefficient (MCC), and mean absolute error. Results: A single GRF feature (propulsive peak) achieved 90.9% accuracy, 86.7% balanced accuracy, and 0.868 MCC for distinguishing PAD from controls and predicting treatment, outperforming patient-reported outcomes and kinematics. For outcome forecasting, a Random Forest model predicted changes in the propulsive peak with 80% accuracy and low error. Conclusions: GRF-based models accurately identify PAD, anticipate this clinical team’s treatment selection, and forecast functional recovery within this single-center cohort; generalization to other practice patterns and settings remains untested. The approach relies on minimal features and modest computation and is readily generalized to other medical conditions that affect gait. Because it is compatible with wearable sensors, it is well suited to real-time, remote assessment and can advance personalized, data-driven decision support in vascular care.