Abstract 4136273: A Machine Learning Approach to Simplify Risk Stratification of Patients with Atherosclerotic Cardiovascular Disease

Introduction: The 2018 AHA/ACC/Multisociety Blood Cholesterol Guideline recommends that patients with atherosclerotic cardiovascular disease (ASCVD) be classified as very high-risk (VHR) or not very high-risk (NVHR). This designation is based on the presence of > 2 major ASCVD events (recent acute coronary syndrome [ACS], history of myocardial infarction [MI], history of ischemic stroke, symptomatic peripheral artery disease [PAD]) or 1 major ASCVD event and > 2 high-risk conditions (age ≥65 years, heterozygous familial hypercholesterolemia, history of coronary revascularization, diabetes, hypertension, chronic kidney disease, current smoking, persistently elevated LDL-cholesterol [LDL-C], history of heart failure). While this approach was initially instituted to better predict future ASCVD events alone, more intensive LDL-C lowering is now recommended for those at VHR (LDL-C treatment threshold of 55 mg/dL for VHR patients vs 70 mg/dL for NVHR patients). We aimed to simplify ASCVD risk stratification in patients with clinical ASCVD using machine learning models. Methods: We used electronic health record data (Providence Health system) from 2022 to identify patients with ASCVD based on ICD-10 codes. Patient demographics, comorbidities, and laboratory data stratified patients as VHR or NVHR. Then, a classification and regression tree analysis was performed, with the outcome being VHR classification. Patients were randomly assigned to either the testing or training dataset in equal proportion. Variables in the model included age, sex, race, ethnicity, and the Guideline criteria. Model performance was assessed using misclassification rate and area under the receiver operative curve (AUC). Results: A total of 491,514 patients with ASCVD were included, of which 56% were male, 77% were white, and the mean (SD) age was 72.5 (13.0) years. Over half of patients (59%) were VHR. Those with recent ACS, history of MI, history of ischemic stroke, or PAD were classified as VHR 93%, 92%, 84%, and 91%, respectively. Presence of hypertension in these patients increased the likelihood of being VHR to 97%, 97%, 94%, and 98%, respectively. The misclassification rate and AUC for the testing set were 6.8% and 96.2%, respectively. Conclusions: Most patients with a major ASCVD event and hypertension were found to be VHR. Relying on these two factors alone can help simplify risk assessment. This approach may also help increase use of guideline-recommended LDL-C lowering therapy.