ArticleAnnals of surgery open : perspectives of surgical history, education, and clinical approaches2023
Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model.
Article in Annals of surgery open : perspectives of surgical history, education, and clinical approaches, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery.iScience · 2026Article
- Real-time, artificial intelligence-guided intraoperative resuscitation and fluid management in trauma anesthesia.Current opinion in anaesthesiology · 2026Review
- Machine learning for hemodynamic instability prediction and hemorrhage management in trauma and perioperative care.Current opinion in anaesthesiology · 2026Article
- Title: Multimodal Fusion of Clinical and Imaging Data for Early Prediction of Massive Transfusion in Trauma.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Ghost in the Machine: Leveraging artificial intelligence in the trauma bay.Trauma surgery & acute care open · 2026Review
- On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality.BJS open · 2025Article
- Factors in the Initial Resuscitation of Patients With Severe Trauma: The FiiRST-2 Randomized Clinical Trial.JAMA network open · 2025Article
- Viscoelastic Hemostatic Assays are Associated With Mortality and Blood Transfusion in a Multicenter Cohort.Journal of the American College of Emergency Physicians open · 2025Article
- Is pre-hospital haemoglobin an efficient marker of the need for transfusion and haemorrhagic shock in severe trauma patients? A retrospective observational study.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Observational
- MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.PloS one · 2025Article
- London fog here, there and everywhere.Trauma surgery & acute care open · 2024Article
- Identification of major hemorrhage in trauma patients in the prehospital setting: diagnostic accuracy and impact on outcome.Trauma surgery & acute care open · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Develop a novel machine learning (ML) model to rapidly identify trauma patients with severe hemorrhage at risk of early mortality. Background: The critical administration threshold (CAT, 3 or more units of red blood cells in a 60-minute period) indicates severe hemorrhage and predicts mortality, whereas early identification of such patients improves survival. Methods: Patients from the PRospective, Observational, Multicenter, Major Trauma Transfusion and Pragmatic, Randomized Optimal Platelet, and Plasma Ratio studies were identified as either CAT+ or CAT-. Candidate variables were separated into 4 tiers based on the anticipated time of availability during the patient's assessment. ML models were created with the stepwise addition of variables and compared with the baseline performance of the assessment of blood consumption (ABC) score for CAT+ prediction using a cross-validated training set and a hold-out validation test set. Results: Of 1245 PRospective, Observational, Multicenter, Major Trauma Transfusion and 680 Pragmatic, Randomized Optimal Platelet and Plasma Ratio study patients, 1312 were included in this analysis, including 862 CAT+ and 450 CAT-. A CatBoost gradient-boosted decision tree model performed best. Using only variables available prehospital or on initial assessment (Tier 1), the ML model performed superior to the ABC score in predicting CAT+ patients [area under the receiver-operator curve (AUC = 0.71 vs 0.62)]. Model discrimination increased with the addition of Tier 2 (AUC = 0.75), Tier 3 (AUC = 0.77), and Tier 4 (AUC = 0.81) variables. Conclusions: A dynamic ML model reliably identified CAT+ trauma patients with data available within minutes of trauma center arrival, and the quality of the prediction improved as more patient-level data became available. Such an approach can optimize the accuracy and timeliness of massive transfusion protocol activation.
Identifiers
What OpenQuestion holds
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.