Artificial Intelligence to Predict Quality-of-Life Outcomes for Vascular Intervention of the Leg

Abstract

BACKGROUND: Artificial intelligence (AI) tools created to enhance decision-making may have a significant impact on treatment algorithms for peripheral arterial disease (PAD). A Markov-based AI model was developed to predict optimal therapy based on maximization of calculated quality of life (cQoL), a patient-centered system of assessment designed to report outcomes directly linked to health-related quality of life. STUDY DESIGN: The AI model was prospectively interrogated immediately after individual interventions for PAD over a 12-year period to test predictive performance. Patient cQoL was determined at each patient follow-up visit. RESULTS: A total of 1,143 consecutive patients were evaluated, with a median follow-up of 18 months. Observed mean annualized cQoL was higher than predicted by the model (0.85 ± 0.38 vs 0.79 ± 0.18, p < 0.0001). Of 5 potential clinical outcomes, the AI model correctly predicted final status in 71.3% of patients, with insignificant model performance deterioration over time (−0.15% per month, r = −0.49, p = 0.063). The chance of having the condition predicted by the model was 0.57 ± 0.32, compared with a theoretical maximum of 0.70 ± 0.19 (p < 0.0001, mean ratio 0.79). The AI model performed better in patients with claudication than limb-threatening ischemia (75.5% vs 63.6%, p = 0.014) but equally well for open or endovascular intervention (69.8% vs 70.5%, p = 0.70). Graft or artery patency and amputation-free survival were better for patients with claudication and those treated with endovascular techniques. CONCLUSIONS: AI can successfully predict treatment for PAD that maximizes patient quality of life in most cases. Future application of AI incorporating better estimates of patient anatomic and physiological risk factors and refinement of model structure should further enhance performance.