Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study
Our prediction models outperformed the widely used Revised Cardiac Risk Index in predicting MACCE within 30 days after noncardiac surgery, demonstrating superior calibration and generalizability across institutions. Its use can optimize preoperative evaluations, minimize unnecessary testing, and streamline perioperative care, significantly improving patient outcomes and resource use. We anticipate that applying this model to actual electronic health records will benefit clinical practice.
Authors
- Wongeun Song (ORCID: https://orcid.org/0000-0003-4314-3995)
- Ji Seon Oh (ORCID: https://orcid.org/0000-0002-0205-6492)
- Jung‐Won Suh (ORCID: https://orcid.org/0000-0002-0397-6071)
- Ju-Seung Kwun (ORCID: https://orcid.org/0000-0002-9581-4060)
- Si‐Hyuck Kang (ORCID: https://orcid.org/0000-0003-4755-6367)
- Sooyoung Yoo (ORCID: https://orcid.org/0000-0001-8620-4925)
- Junho Hyun (ORCID: https://orcid.org/0000-0003-4211-3081)
- Seok Kim (ORCID: https://orcid.org/0000-0003-4996-8613)
- H Ahn
- Gakyoung Baek
Institutions
- Ulsan College (KR)
- Seoul National University of Science and Technology (KR)
- Seoul National University (KR)
- Asan Medical Center (KR)
- Seoul National University Bundang Hospital (KR)
- University of Ulsan (KR)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-04-09
- DOI
- https://doi.org/10.2196/66366
- Citations
- 2
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.78