Abstract
Risk prediction following acute coronary syndrome (ACS) remains challenging. Data-driven machine-learning algorithms can potentially identify patients at high risk of clinical events. The Improved Reduction of Outcomes: Vytorin Efficacy International Trial randomized 18,144 post-ACS patients to ezetimibe + simvastatin or placebo + simvastatin. We performed hierarchical cluster analysis to identify patients at high risk of adverse events. Associations between clusters and outcomes were assessed using Cox proportional hazards models. The primary outcome was cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, unstable angina hospitalization, or coronary revascularization ≥30 days after randomization. We evaluated ezetimibe's impact on outcomes across clusters and the ability of the cluster analysis to discriminate for outcomes compared with the Global Registry of Acute Coronary Events (GRACE) score. Five clusters were identified. In cluster 1 (n = 13,252), most patients experienced a non-STEMI (54.8%). Cluster 2 patients (n = 2,719) had the highest incidence of unstable angina (n = 83.3%). Cluster 3 patients (n = 782) all identified as Spanish descent, whereas cluster 4 patients (n = 803) were primarily from South America (56.2%). In cluster 5 (n = 587), all patients had ST elevation. Cluster analysis identified patients at high risk of adverse outcomes (log-rank p <0.0001); Cluster 2 (vs 1) patients had the highest risk of outcomes (hazards ratio 1.33, 95% confidence interval 1.24 to 1.43). Compared with GRACE risk, cluster analysis did not provide superior outcome discrimination. A consistent ezetimibe treatment effect was identified across clusters (interaction p = 0.882). In conclusion, cluster analysis identified significant difference in risk of outcomes across cluster groups. Data-driven strategies to identify patients who may differentially benefit from therapies and for risk stratification require further evaluation.