Unsupervised cardiometabolic phenotyping unmasks residual MACCE risk beyond LDL-C in acute myocardial infarction after revascularization

Background Post-percutaneous coronary intervention (PCI) outcomes in acute myocardial infarction (AMI) remain heterogeneous despite guideline-directed therapy and lower low-density lipoprotein cholesterol (LDL-C) targets. We employed unsupervised clustering of cardiometabolic biomarkers to uncover novel clinical phenotypes that conventional stratifications overlook. Methods K-means clustering was applied to 13 standardized cardiometabolic variables in 668 AMI patients who underwent successful PCI. Cluster stability was assessed by bootstrap resampling. Clinical outcomes were evaluated by Kaplan–Meier analysis and multivariable Cox regression. Receiver operating characteristic curves and DeLong testing compared discriminative performance between phenotype classification and LDL-C target attainment. Results Three phenotypes were identified: Ph0 Metabolically Balanced ( n = 391, 58.5%), Ph1 Hyperglycemic-Dyslipidemic ( n = 107, 16.0%), and Ph2 Decompensated Inflammatory-Catabolic ( n = 170, 25.4%). Over a median follow-up of 31.9 months, major adverse cardiovascular and cerebrovascular events (MACCE) occurred in 19.4%, 31.8%, and 49.4%, respectively ( P < 0.001). After full covariate adjustment, Ph2 remained independently associated with MACCE (hazard ratio 2.97, 95% confidence interval 2.00–4.41, P < 0.001), while Ph1 was attenuated to non-significance ( P = 0.136). Despite having the lowest LDL-C, Ph2 carried the highest event rate. LDL-C target attainment (