In International journal of cardiology ; h5-index 68.0
INTRODUCTION : Percutaneous coronary interventions (PCI) are often performed in multimorbid patients with heterogeneous 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.
Galimzhanov Akhmetzhan, Sabitov Yersin, Guclu Elif, Tenekecioglu Erhan, Mamas Mamas A
2022-Dec-24
Cluster analyses, Coronary artery disease, Machine learning, Percutaneous coronary intervention, Prognosis
