Multicenter Validation of a Machine Learning Model for Surgical Transfusion Risk at 45 US Hospitals
In this cohort study of 45 hospitals, a personalized surgical transfusion risk prediction algorithm demonstrated external validity and discrimination. S-PATH was consistently more effective than standard care, suggesting its potential for use as a perioperative clinical decision support tool.
Authors
- Michael R. Mathis (ORCID: https://orcid.org/0000-0002-9697-2212)
- Michael S. Avidan (ORCID: https://orcid.org/0000-0001-6248-044X)
- Vikas N. O’Reilly-Shah (ORCID: https://orcid.org/0000-0003-0741-0291)
- Robert E. Freundlich (ORCID: https://orcid.org/0000-0002-0343-9823)
- Charles W. Goss (ORCID: https://orcid.org/0000-0003-2682-7316)
- Sunny S. Lou (ORCID: https://orcid.org/0000-0002-4215-605X)
- Thomas Kannampallil (ORCID: https://orcid.org/0000-0003-4119-4836)
- Sayantan Kumar (ORCID: https://orcid.org/0000-0001-7213-0734)
- Kunal Karamchandani (ORCID: https://orcid.org/0000-0003-3089-8088)
- Sachin Kheterpal
Institutions
- Washington University in St. Louis (US)
- University of Michigan (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.17760
- Citations
- 4
- Primary Topic
- Healthcare cost, quality, practices
- Type
- article
- Field-Weighted Citation Impact
- 11.26