Are PCI Service Volumes Associated with 30-Day Mortality? A Population-Based Study from Taiwan.
Tsung-Hsien YuYing-Yi ChouChung-Jen WeiYu-Chi TungPublished in: International journal of environmental research and public health (2017)
The volume-outcome relationship has been discussed for over 30 years; however, the findings are inconsistent. This might be due to the heterogeneity of service volume definitions and categorization methods. This study takes percutaneous coronary intervention (PCI) as an example to examine whether the service volume was associated with PCI 30-day mortality, given different service volume definitions and categorization methods. A population-based, cross-sectional multilevel study was conducted. Two definitions of physician and hospital volume were used: (1) the cumulative PCI volume in a previous year before each PCI; (2) the cumulative PCI volume within the study period. The volume was further treated in three ways: (1) a categorical variable based on the American Heart Association's recommendation; (2) a semi-data-driven categorical variable based on k-means clustering algorithm; and (3) a data-driven categorical variable based on the Generalized Additive Model. The results showed that, after adjusting the patient-, physician-, and hospital-level covariates, physician volume was associated inversely with PCI 30-day mortality, but hospital volume was not, no matter which definitions and categorization methods of service volume were applied. Physician volume is negatively associated with PCI 30-day mortality, but the results might vary because of definition and categorization method.
Keyphrases
- percutaneous coronary intervention
- coronary artery disease
- acute coronary syndrome
- acute myocardial infarction
- healthcare
- st segment elevation myocardial infarction
- antiplatelet therapy
- st elevation myocardial infarction
- primary care
- mental health
- atrial fibrillation
- emergency department
- cardiovascular events
- coronary artery bypass grafting
- cross sectional
- cardiovascular disease
- machine learning
- left ventricular
- risk factors
- deep learning
- case report