ArticleThe journal of trauma and acute care surgery2013
Let technology do the work: Improving prediction of massive transfusion with the aid of a smartphone application.
Article in The journal of trauma and acute care surgery, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review.BMJ global health · 2019Pooled it
- Early prediction of transfusion requirements in trauma patients using explainable machine learning.Transfusion · 2026Article
- MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.PloS one · 2025Article
- Digital twin mathematical models suggest individualized hemorrhagic shock resuscitation strategies.Communications medicine · 2024Article
- Identification of major hemorrhage in trauma patients in the prehospital setting: diagnostic accuracy and impact on outcome.Trauma surgery & acute care open · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- AI algorithm for personalized resource allocation and treatment of hemorrhage casualties.Frontiers in physiology · 2024Article
- Assessment of Machine Learning Methods to Predict Massive Blood Transfusion in Trauma.World journal of surgery · 2023Article
- Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2023Article
- Artificial intelligence and machine learning for hemorrhagic trauma care.Military Medical Research · 2023Review
- Massive Transfusion Protocol Predictive Modeling in the Modern Electronic Medical Record.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2021Article
- [Computer-assisted decision-making for trauma patients].Der Unfallchirurg · 2020Review
- Mobile application-based guidelines to enhance patient care and provider education in trauma and acute care surgery.Trauma surgery & acute care open · 2020Article
- External validation of a smartphone app model to predict the need for massive transfusion using five different definitions.The journal of trauma and acute care surgery · 2018Observational
Corrections and comments
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Authors and funding
3 authors.
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Abstract
backgroundThe use of massive transfusion protocols (MTPs) is now common in civilian trauma settings, and early activation of MTP has been shown to increase survival of MTP recipients. Numerous MTP prediction tools have been developed; however, they are often cumbersome to use efficiently or have traded predictive power for ease of use. We hypothesized that a highly accurate predictor of massive transfusion could be created and incorporated into a smartphone application that would provide an additional tool for clinicians to use in directing the resuscitation of critically injured patients.
methodsData from all trauma admissions since the inception of MTP were put in place at Grady Memorial Hospital in Atlanta, Georgia, were collected. A predictive model was developed using the least absolute shrinkage and selection operator (LASSO) and 10-fold cross validation. Data were resampled over 500 iterations, each using a unique and random subset of 80% of the data for model training and 20% for validation.
resultsThe trauma registry contained 13,961 cases between 2007 and November 2011, of which 10,900 were complete and 394 received MTP. Of 44 input terms, only the mechanism of injury, heart rate, systolic blood pressure, and base deficit were found to be important predictors of massive transfusion. Our model has an area under the receiver operating curve of 0.96 (against data not used during model training) and accurately predicted MTP status for 97% of all patients. The model accurately discriminated full MTPs from MTP activations that did not meet criteria for massive transfusion. While complex to calculate by hand, our model has been packaged into a mobile application, allowing for efficient use while minimizing potential for user error.
conclusionWe have developed a highly accurate model for the prediction of massive transfusion that has potential to be easily accessed and used within a simple and efficient mobile application for smartphones. LEVEL OF EVIDENCE: Prognostic/epidemiologic study, level III.
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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.