Phenotyping for percutaneous coronary intervention and long-term recurrent weighted outcomes

Introduction: Percutaneous coronary interventions (PCI) are often performed in multimorbid patients with het-erogeneous characteristics and variable clinical outcomes. We aimed to identify distinct clinical phenotypes utilizing machine learning and explore their relationship with long-term recurrent and weighted outcomes.Methods: This prospective observational cohort study enrolled all-comer PCI patients in 2020-2021. Multiple imputation k-means clustering was utilized to detect specific phenotypes. The study endpoints were patient -oriented and device oriented composite endpoints (POCE, DOCE), its individual components, and major bleeding. We applied semiparametric regression models for recurrent and weighted endpoints.Results: The study included a total of 643 patients. We unveiled three phenotype clusters: 1) inflammatory (n = 44, with high white blood cell counts, high values of C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio), 2) high erythrocyte sedimentation rate (ESR) (n = 204), and 3) non-inflammatory (n = 395). For ACS -only population, we four distinct phenotypes (high-CRP, high-ESR, high aspartate-aminotransferase, and normal). For all-comer PCI patients, identified phenotypes had a higher risk of POCE (mean ratio (MR) 1.42 (95% confidence interval (CI) 1.11-1.81) and MR 2.01 (95% CI 1.58-2.56), respectively), DOCE (MR 1.61 (95% CI 1.20-2.16), MR 2.60 (95%CI 1.94-3.48), respectively), and stroke (hazard ratio (HR) 2.86 (95% CI 1.10-7.4), 6.83 (95% CI 2.01-23.2)). Similarly, high-ESR and high-CRP phenotypes of ACS patients were significantly associated with the development of clinical composite outcomes.Conclusion: Machine learning unveiled three distinct phenotype clusters in patients after PCI that were linked with the risk of recurrent and weighted clinical endpoints.German Clinical Trial Registry number: DRKS00020892.