Trial reportTransfusion2020
Machine learning-based prediction of transfusion.
Trial report in Transfusion, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 42 citations in OpenAlex.
- Machine learning-based prediction of transfusion.Transfusion · 2020Trial
- Analysis of the Efficacy of Red Blood Cell Transfusion in Very Low Birth Weight Infants and Construction of a Prediction Model.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2026Article
- AI and Blood Banking: Predicting Transfusion Demand - A Systematic Review of Forecasting Approaches.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026Review
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A Machine Learning Approach Using Heart Rate Variability.Critical care explorations · 2025Article
- Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.Hematology, transfusion and cell therapy · 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
- Markers of Futile Resuscitation in Traumatic Hemorrhage: A Review of the Evidence and a Proposal for Futility Time-Outs during Massive Transfusion.Journal of clinical medicine · 2024Review
- Evaluating the Shock Index, Revised Assessment of Bleeding and Transfusion (RABT), Assessment of Blood Consumption (ABC) and novel PTTrauma score to predict critical transfusion threshold (CAT) in penetrating thoracic trauma.Scientific reports · 2024Observational
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.Frontiers in cardiovascular medicine · 2024Article
- Robust Meta-Model for Predicting the Likelihood of Receiving Blood Transfusion in Non-traumatic Intensive Care Unit Patients.Health data science · 2024Article
- Construction and effect evaluation of prediction model for red blood cell transfusion requirement in cesarean section based on artificial intelligence.BMC medical informatics and decision making · 2023Article
- The Impact of Digital Transformation on Inpatient Care: Mixed Methods Study.JMIR public health and surveillance · 2023Review
- Multivariable statistical models to predict red cell transfusion in elective surgery.Blood transfusion = Trasfusione del sangue · 2023Article
- Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units.Healthcare informatics research · 2022Article
- Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders.Anesthesiology · 2022Article
- Development and validation of a machine learning method to predict intraoperative red blood cell transfusions in cardiothoracic surgery.Scientific reports · 2022Article
- Modifiable Individual Risks of Perioperative Blood Transfusions and Acute Postoperative Complications in Total Hip and Knee Arthroplasty.Journal of personalized medicine · 2021Article
- Blood usage at a large academic center in Maryland in relation to the COVID-19 pandemic in 2020.Transfusion · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe ability to predict transfusions arising during hospital admission might enable economized blood supply management and might furthermore increase patient safety by ensuring a sufficient stock of red blood cells (RBCs) for a specific patient. We therefore investigated the precision of four different machine learning-based prediction algorithms to predict transfusion, massive transfusion, and the number of transfusions in patients admitted to a hospital. STUDY DESIGN AND
methodsThis was a retrospective, observational study in three adult tertiary care hospitals in Western Australia between January 2008 and June 2017. Primary outcome measures for the classification tasks were the area under the curve for the receiver operating characteristics curve, the F
resultsUsing our four predictive models, transfusion of at least 1 unit of RBCs could be predicted rather accurately (sensitivity for NN, LR, RF, and GB: 0.898, 0.894, 0.584, and 0.872, respectively; specificity: 0.958, 0.966, 0.964, 0.965). Using the four methods for prediction of massive transfusion was less successful (sensitivity for NN, LR, RF, and GB: 0.780, 0.721, 0.002, and 0.797, respectively; specificity: 0.994, 0.995, 0.993, 0.995). As a consequence, prediction of the total number of packed RBCs transfused was also rather inaccurate.
conclusionThis study demonstrates that the necessity for intrahospital transfusion can be forecasted reliably, however the amount of RBC units transfused during a hospital stay is more difficult to predict.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.