Using a cohort study of diabetes and peripheral artery disease to compare logistic regression and machine learning via random forest modeling

作者：Andrea M. Austin, Niveditta Ramkumar, Barbara Gladders, Jonathan Barnes, Mark A. Eid, Kayla Moore, Mark W. Feinberg, Mark A. Creager, Marc P. Bonaca, Philip P. Goodney · 发表于：BMC Medical Research Methodology · 年份：2022 · DOI：10.1186/s12874-022-01774-8 · 被引用次数：36 · 研究领域：Peripheral Artery Disease Management、Diabetic Foot Ulcer Assessment and Management、HIV-related health complications and treatments

BACKGROUND: This study illustrates the use of logistic regression and machine learning methods, specifically random forest models, in health services research by analyzing outcomes for a cohort of patients with concomitant peripheral artery disease and diabetes mellitus. METHODS: Cohort study using fee-for-service Medicare beneficiaries in 2015 who were newly diagnosed with peripheral artery disease and diabetes mellitus. Exposure variables include whether patients received preventive measures in the 6 months following their index date: HbA1c test, foot exam, or vascular imaging study. Outcomes include any reintervention, lower extremity amputation, and death. We fit both logistic regression models as well as random forest models. RESULTS: There were 88,898 fee-for-service Medicare beneficiaries diagnosed with peripheral artery disease and diabetes mellitus in our cohort. The rate of preventative treatments in the first six months following diagnosis were 52% (n = 45,971) with foot exams, 43% (n = 38,393) had vascular imaging, and 50% (n = 44,181) had an HbA1c test. The directionality of the influence for all covariates considered matched those results found with the random forest and logistic regression models. The most predictive covariate in each approach differs as determined by the t-statistics from logistic regression and variable importance (VI) in the random forest model. For amputation we see age 85 + (t = 53.17) urban-residing (VI = 83.42), and for death (t = 65.84,...