OBJECTIVES: Survival of patients with atherosclerotic cardiovascular disease (ASCVD) has increased; understanding the burden of subsequent events is important. Research is lacking regarding predictors of recurrent coronary risk. Our objective was to develop and validate a prediction model for recurrent CV events among patients with established ASCVD. METHODS: We conducted a retrospective cohort study using data from Truven Health MarketScan® Commercial Claims and Encounters. Adults (≥18 years) diagnosed with ASCVD in 2012 (index date) with at least one LDL-C measurement in the 12 months prior to the index date were included. Patients were followed for the occurrence of a subsequent CV event (myocardial infarction, stroke, hospitalization for unstable angina, stroke, or coronary revascularization), the end of enrollment, or the end of 12-months follow up. We used Cox proportional hazards regression to evaluate the predictive ability of clinical and laboratory risk factors and selected the model with the highest discriminatory ability. Temporal validation was performed using two cohorts of patients in 2011 and 2013. We assessed discrimination and calibration in all three cohorts. RESULTS: A total of 49,651 patients met our inclusion criteria. Strong predictors of a subsequent CV events included older age (≥66 years), male gender, geographical location, LDL-C above 100 mg/dL, history of cancer, chronic kidney disease, and a major cardiovascular event. High potency statin treatment (HR 0.75; 95% CI 0.67-0.84) and moderate potency statin treatment (HR 0.85; 95% CI 0.77-0.94) were significantly associated with reduced risk of recurrent CV events. The prediction model showed a reasonable C-statistic (0.70, 95% CI 0.66-0.74) and had an acceptable discrimination in the two validation cohorts (0.58, 95% CI 0.53-0.52; and 0.53, 95% CI 0.49-0.57, respectively). CONCLUSIONS: This risk prediction model of recurrent CV events may be informative in evaluating short-term prognosis and treatment decisions among patients with ASCVD in real-world clinical settings.