Iterative random forest-based identification of a novel population with high risk of complications post non-cardiac surgery.

ABSTRACT: Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying iterative random forest to analyze combinations of factors that could potentially be clinically valuable in identifying these high-risk populations. To this end, we used the Japan Medical Data Center database, which includes claims data from Japan between January 2005 and April 2021, and employed iterative random forests to extract factor combinations that influence outcomes. The analysis demonstrated that a combination of a prior history of stroke and extremely low LDL-C levels was associated with a hi

SUBMITTER: Seki T 

PROVIDER: S-EPMC11538396 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature