Multicenter validation of a machine learning model for surgical transfusion risk at 45 US hospitals - Open Access Publication

Aug 5, 2026

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Accurate estimation of surgical transfusion risk supports perioperative planning and resource allocation, yet many healthcare machine learning models lack external validation or show limited performance outside development settings. This retrospective cohort externally validated the publicly available S-PATH algorithm to estimate red cell transfusion during surgery across a national sample of 45 US hospitals in 2020–2021. Using hospital-specific performance evaluation at a 96% sensitivity threshold, results showed consistent discrimination and external validity, with S-PATH outperforming standard-of-care MSBOS in recommending type and screen orders.

LanguageEnglish

CategoryResearch & Report

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Pages14

Reading Time35 min

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Abstract

Importance, objective, and design

Exposures and evaluation metrics

Results and conclusions

Key points

Washington University School of Medicine Digital Commons@Becker | 2020-Current year OA Pubs | Open Access Publications | | --- | --- | | 6-2-2025
Multicenter validation of a machine learning model for surgical transfusion risk at 45 US hospitals
Sunny S Lou
Washington University School of Medicine in St. Louis Sayantan Kumar
Washington University School of Medicine in St. Louis Charles W Goss
Washington University School of Medicine in St. Louis Michael S Avidan
Washington University School of Medicine in St. Louis Sachin Kheterpal
University of Michigan-Ann Arbor
See next page for additional authors
Follow this and additional works at: [https://digitalcommons.wustl.edu/oa_4](https://digitalcommons.wustl.edu/oa_4)
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Please let us know how this document benefits you. | | Recommended Citation Lou, Sunny S; Kumar, Sayantan; Goss, Charles W; Avidan, Michael S; Kheterpal, Sachin; Kannampallil, Thomas; and Multicenter Perioperative Outcomes Group, "Multicenter validation of a machine learning model for surgical transfusion risk at 45 US hospitals." JAMA Network Open. 8, 6. e2517760 (2025) . [https://digitalcommons.wustl.edu/oa_4/5228](https://digitalcommons.wustl.edu/oa_4/5228) This Open Access Publication is brought to you for free and open access by the Open Access Publications at Digital Commons@Becker. It has been accepted for inclusion in 2020-Current year OA Pubs by an authorized administrator of Digital Commons@Becker. For more information, [please contact](please contact vanam@wustl.edu)[ vanam@wustl.edu](please contact vanam@wustl.edu). Authors Sunny S Lou, Sayantan Kumar, Charles W Goss, Michael S Avidan, Sachin Kheterpal, Thomas Kannampallil, and Multicenter Perioperative Outcomes Group This open access publication is available at Digital Commons@Becker: [https://digitalcommons.wustl.edu/oa_4/5228](https://digitalcommons.wustl.edu/oa_4/5228) Original Investigation | Health Informatics Multicenter Validation of a Machine Learning Model for Surgical Transfusion Risk at 45 US Hospitals Sunny S. Lou, MD, PhD; Sayantan Kumar, PhD; Charles W. Goss, PhD; Michael S. Avidan, MBBCh; Sachin Kheterpal, MD, MBA; Thomas Kannampallil, PhD; for the Multicenter Perioperative Outcomes Group | Abstract
IMPORTANCE Accurate estimation of surgical transfusion risk is important for perioperative planning and effective resource allocation. Most machine learning models in health care are not validated or perform poorly in external settings.

OBJECTIVE To externally validate a publicly available machine learning algorithm (Surgical Personalized Anticipation of Transfusion Hazard [S-PATH]) to estimate red cell transfusion during surgery within a national sample of hospitals.

DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study evaluated all surgical cases performed in 2020 or 2021 at 45 US hospitals participating in the Multicenter Perioperative Outcomes Group. Obstetric and nonoperative cases were excluded. Data analysis was performed from February 2023 to March 2025 .

EXPOSURES At each hospital, S-PATH was used to estimate surgical transfusion risk using patientand procedure-specific characteristics without local retraining. A baseline model representing the standard-of-care maximum surgical blood ordering schedule (MSBOS) approach, which omits patient factors, was used for comparison. Risk thresholds above which a type and screen would be recommended were set for 96% sensitivity. Performance was evaluated at each hospital separately.

MAIN OUTCOMESAND MEASURES The primary outcome was the difference in the percentage of patients with type and screen order recommendations between S-PATHand MSBOSat each hospital. The secondary outcome was area under the receiver operating characteristic curve (AUROC) .

RESULTS In this cohort study of 3275956 surgical cases (median [IQR] age, 57 [40-69] years; 53

FAQs

What was the main purpose of this study?

To externally validate the publicly available S-PATH machine learning algorithm for estimating surgical transfusion risk during surgery across hospitals in the United States.

How was S-PATH compared with standard care?

S-PATH was used at each hospital to estimate risk without local retraining, and performance was compared against the MSBOS baseline approach using hospital-specific evaluation at matched sensitivity.

What were the study’s key findings?

Across 45 hospitals, S-PATH recommended type and screen orders for fewer patients than the standard-of-care approach while maintaining the same 96% sensitivity, and showed stronger discrimination based on AUROC.

Aug 5, 2026

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