Long‐Term Mortality Predictors Using a Machine‐Learning Approach in Patients With Chronic Limb‐Threatening Ischemia After Peripheral Vascular Intervention

Background Patients with chronic limb‐threatening ischemia (CLTI) face a high long‐term mortality risk. Identifying novel mortality predictors and risk profiles would enable individual health care plan design and improved survival. We aimed to leverage a random survival forest machine‐learning algorithm to identify long‐term all‐cause mortality predictors in patients with CLTI undergoing peripheral vascular intervention. Methods and Results Patients with CLTI undergoing peripheral vascular intervention from 2017 to 2018 were derived from the Medicare‐linked VQI (Vascular Quality Initiative) registry. We constructed a random survival forest to rank 66 preprocedural variables according to their relative importance and mean minimal depth for 3‐year all‐cause mortality. A random survival forest of 2000 trees was built using a training sample (80% of the cohort). Accuracy was assessed in a testing sample (20%) using continuous ranked probability score, Harrell C‐index, and out‐of‐bag error rate. A total of 10 114 patients were included (mean±SD age, 72.0±11.0 years; 59% men). The 3‐year mortality rate was 39.1%, with a median survival of 1.4 years (interquartile range, 0.7–2.0 years). The most predictive variables were chronic kidney disease, age, congestive heart failure, dementia, arrhythmias, requiring assisted care, living at home, and body mass index. A total of 41 variables spanning all domains of the biopsychosocial model were ranked as mortality predictors. The accuracy of the model was excellent (continuous ranked probability score, 0.172; Harrell C‐index, 0.70; out‐of‐bag error rate, 29.7%). Conclusions Our random survival forest accurately predicts long‐term CLTI mortality, which is driven by demographic, functional, behavioral, and medical comorbidities. Broadening frameworks of risk and refining health care plans to include multidimensional risk factors could improve individualized care for CLTI.