Evaluation of short-term mortality in patients with Medicare undergoing endovascular interventions for chronic limb-threatening ischemia

Abstract

Introduction: Patients with chronic limb-threatening ischemia (CLTI) have high mortality rates after revascularization. Risk stratification for short-term outcomes is challenging. We aimed to develop machine-learning models to rank predictive variables for 30-day and 90-day all-cause mortality after peripheral vascular intervention (PVI). Methods: Patients undergoing PVI for CLTI in the Medicare-linked Vascular Quality Initiative were included. Sixty-six preprocedural variables were included. Random survival forest (RSF) models were constructed for 30-day and 90-day all-cause mortality in the training sample and evaluated in the testing sample. Predictive variables were ranked based on the frequency that they caused branch splitting nearest the root node by importance-weighted relative importance plots. Model performance was assessed by the Brier score, continuous ranked probability score, out-of-bag error rate, and Harrell’s C-index. Results: A total of 10,114 patients were included. The crude mortality rate was 4.4% at 30 days and 10.6% at 90 days. RSF models commonly identified stage 5 chronic kidney disease (CKD), dementia, congestive heart failure (CHF), age, urgent procedures, and need for assisted care as the most predictive variables. For both models, eight of the top 10 variables were either medical comorbidities or functional status variables. Models showed good discrimination (C-statistic 0.72 and 0.73) and calibration (Brier score 0.03 and 0.10). Conclusion: RSF models for 30-day and 90-day all-cause mortality commonly identified CKD, dementia, CHF, need for assisted care at home, urgent procedures, and age as the most predictive variables as critical factors in CLTI. Results may help guide individualized risk-benefit treatment conversations regarding PVI.

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

“…We build on existing risk prediction models by including a comprehensive and multidimensional set of preprocedural variables, including behavioral health comorbidities, available to clinicians at the time of evaluation before a decision on revascularization has been reached. 45 , 46 , 47 , 48 The use of random forest survival models in this study to rank predictive variables has not previously been used to evaluate long‐term mortality risk factors in CLTI.…”