Abstract
To reduce unnecessary delays and manage medical costs efficiently for low-risk patients undergoing noncardiac surgery, we developed a predictive model for major adverse cardiac and cerebrovascular events (MACCE) using the OMOP Common Data Model (CDM) and machine learning algorithms. This retrospective study collected data from 46,225 patients at Seoul National University Bundang Hospital and 396,424 patients at Asan Medical Center. Patients aged 65 or older undergoing non-cardiac, non-emergency surgeries with at least 30 days of observation were included. Machine learning models were developed using the OHDSI open-source patient-level prediction package in R version 4.1.0. All models outperformed the Revised Cardiac Risk Index (RCRI), with the random forest model achieving an AUROC of 0.817 in external validation and demonstrating moderate calibration. Key predictors included previous diagnoses and laboratory measurements, highlighting their importance in perioperative risk prediction. Our model shows promise for improving clinical practice and reducing medical costs.