ArticleJournal of clinical anesthesia2023
Development of a machine learning model to predict intraoperative transfusion and guide type and screen ordering.
Article in Journal of clinical anesthesia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning-based prediction of intraoperative blood transfusion in major surgery: exploiting clinical variables and systemic inflammatory indices.Annals of medicine · 2026Article
- Prediction of Blood Transfusion Need and Dose in Patients With Upper Gastrointestinal Bleeding: Retrospective Multicenter Prediction Model Study.JMIR medical informatics · 2026Article
- Machine Learning for Intraoperative Bleeding Prediction in Patients Undergoing Surgery: Scoping Review.JMIR medical informatics · 2026Article
- Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Review
- Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology.Frontiers in medicine · 2025Review
- Construction and validation of a perioperative blood transfusion model for patients undergoing total hip arthroplasty with osteonecrosis of the femoral head based on machine learning.Frontiers in medicine · 2025Article
- Challenges in reliable preoperative blood ordering: A qualitative interview study.Transfusion · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
STUDY
objectiveTo develop an algorithm to predict intraoperative Red Blood Cell (RBC) transfusion from preoperative variables contained in the electronic medical record of our institution, with the goal of guiding type and screen ordering.
designMachine Learning model development on retrospective single-center hospital data.
settingPreoperative period and operating room. PATIENTS: The study included patients ≥18 years old who underwent surgery during 2019-2022 and excluded those who refused transfusion, underwent emergency surgery, or surgery for organ donation after cardiac or brain death.
interventionPrediction of intraoperative transfusion vs. no intraoperative transfusion. MEASUREMENTS: The outcome variable was intraoperative transfusion of RBCs. Predictive variables were surgery, surgeon, anesthesiologist, age, sex, body mass index, race or ethnicity, preoperative hemoglobin (g/dL), partial thromboplastin time (s), platelet count x 10 MAIN
resultsOut of 100,813 cases that met study criteria from 2019 to 2021, intraoperative transfusion occurred in 5488 (5.4%) of cases. The LightGBM model was the highest performing algorithm in external temporal validity experiments, with overall accuracy of (76.1%) [95% confidence interval (CI), 75.6-76.5], while maintaining clinically relevant sensitivity of (91.2%) [95% CI, 89.8-92.5]. If type and screens were ordered based upon the LightGBM model, the predicted type and screen to transfusion ratio would improve from 8.4 to 5.1.
conclusionsMachine learning approaches are feasible in predicting intraoperative transfusion from preoperative variables and may improve preoperative type and screen ordering practices when incorporated into the electronic health record.
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