ArticleScientific reports2022
Development and validation of a machine learning method to predict intraoperative red blood cell transfusions in cardiothoracic surgery.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 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
- Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery.iScience · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.BMC medical informatics and decision making · 2024Article
- Mapping the landscape of machine learning models used for predicting transfusions in surgical procedures: a scoping review.BMC medical informatics and decision making · 2024Article
- Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning.Scientific reports · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- An update on red blood cell transfusion in non-cardiac thoracic surgery.Journal of thoracic disease · 2023Article
- On the Horizon: Specific Applications of Automation and Artificial Intelligence in Anesthesiology.Current anesthesiology reports · 2023Article
- Recent advances in cardiac anaesthesia.Indian journal of anaesthesia · 2023Article
- The future of Cardiothoracic surgery in Artificial intelligence.Annals of medicine and surgery (2012) · 2022Review
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Authors and funding
7 authors.
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Abstract
Accurately predicting red blood cell (RBC) transfusion requirements in cardiothoracic (CT) surgery could improve blood inventory management and be used as a surrogate marker for assessing hemorrhage risk preoperatively. We developed a machine learning (ML) method to predict intraoperative RBC transfusions in CT surgery. A detailed database containing time-stamped clinical variables for all CT surgeries from 5/2014-6/2019 at a single center (n = 2410) was used for model development. After random forest feature selection, surviving features were inputs for ML algorithms using five-fold cross-validation. The dataset was updated with 437 additional cases from 8/2019-8/2020 for validation. We developed and validated a hybrid ML method given the skewed nature of the dataset. Our Gaussian Process (GP) regression ML algorithm accurately predicted RBC transfusion amounts of 0 and 1-3 units (root mean square error, RMSE 0.117 and 1.705, respectively) and our GP classification ML algorithm accurately predicted 4 + RBC units transfused (area under the curve, AUC = 0.826). The final prediction is the regression result if classification predicted < 4 units transfused, or the classification result if 4 + units were predicted. We developed and validated an ML method to accurately predict intraoperative RBC transfusions in CT surgery using local data.
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