A machine learning algorithm-based risk prediction score for in-hospital/30-day mortality after adult cardiac surgery
Feature-selected XGBoost showed good discrimination, calibration and clinical benefit when predicting mortality post-cardiac surgery. Prospective external validation of a XGBoost-derived model performance is warranted.
Authors
- Tim Dong (ORCID: https://orcid.org/0000-0003-1953-0063)
- Arnaldo Dimagli (ORCID: https://orcid.org/0000-0003-0055-8656)
- Gianni D. Angelini (ORCID: https://orcid.org/0000-0002-1753-3730)
- Andrew Judge (ORCID: https://orcid.org/0000-0003-3015-0432)
- Shubhra Sinha (ORCID: https://orcid.org/0000-0001-8554-6704)
Institutions
- University Hospitals Bristol NHS Foundation Trust (GB)
- National Institute for Health and Care Research (GB)
- University of Bristol (GB)
- NIHR Bristol Cardiovascular Biomedical Research Unit (GB)
Publication Details
- Journal
- European Journal of Cardio-Thoracic Surgery
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1093/ejcts/ezae368
- Citations
- 10
- Primary Topic
- Cardiac, Anesthesia and Surgical Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 4.02
Funders
- University Hospitals Bristol NHS Foundation Trust
- National Institute for Health and Care Research
- British Heart Foundation
- University of Bristol