Development and Population-Based Prognostic Utility Assessment of an ICD-10-CM-Based Prediction Model to Identify Left Ventricular Systolic Dysfunction: Discrimination Performance and Population-Based Evaluation in 83,357 Patients with Acute Coronary Syndrome

Abstract

Population-based studies of acute coronary syndrome (ACS) are often limited by the lack of structured left ventricular ejection fraction (LVEF) data in administrative registries. We developed a logistic regression model using ICD-10-CM diagnostic codes and two clinical variables to estimate the probability of reduced LVEF (<50%) at discharge after ACS, and examined the prognostic value of the resulting classification in a large population-based cohort. The model was derived from 1596 patients with pre-discharge echocardiographic LVEF. In a 10-fold cross-validation analysis that repeated predictor selection within each train-ing fold, the procedure yielded a ROC-AUC of 0.711 (95% CI, 0.680–0.741). The fixed 28-predictor model, subsequently applied to the population-based cohort, yielded a ROC-AUC of 0.738 in the corresponding cross-validation analysis. The fixed 28-predictor model was then applied to 83,357 patients from the population-based BEAT-HF cohort (Catalonia, 2011–2021), in whom reference LVEF was unavailable. Patients classified as having reduced LVEF had a less favourable clinical profile and significantly higher rates of adverse cardiovascular outcomes. Predicted reduced LVEF was associated with higher observed rates of all-cause mortality (HR 2.45, 95% CI 2.35–2.55; p < 0.001), as well as higher risks of heart failure (HF) and major adverse cardiovascular events. The model should not guide individual clinical decisions but may support exploratory population-level characterization of patients in administrative data. Validation against measured LVEF is needed.