Development of a field artificial intelligence triage tool: Confidence in the prediction of shock, transfusion, and definitive surgical therapy in patients with truncal gunshot wounds
| Autor: | Jay Roberts, David R. King, Haytham M.A. Kaafarani, Theodoros Tsiligkaridis, Peter J. Fagenholz, Noelle Saillant, George C. Velmahos, Charlie J. Nederpelt, Ava Mokhtari, Miriam Cha, Jonathan Parks, Jason Fawley, April E. Mendoza, Osaid Alser |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Emergency Medical Services Quality management Thoracic Injuries Vital signs MEDLINE Hemorrhage Critical Care and Intensive Care Medicine Risk Assessment Young Adult 03 medical and health sciences Injury Severity Score 0302 clinical medicine Trauma Centers Artificial Intelligence Humans Medicine Blood Transfusion Retrospective Studies Receiver operating characteristic Artificial neural network business.industry Models Cardiovascular Shock 030208 emergency & critical care medicine Evidence-based medicine medicine.disease Triage ROC Curve Feasibility Studies Female Wounds Gunshot Surgery Artificial intelligence Gunshot wound business |
| Zdroj: | Journal of Trauma and Acute Care Surgery. 90:1054-1060 |
| ISSN: | 2163-0763 2163-0755 |
| Popis: | Background In-field triage tools for trauma patients are limited by availability of information, linear risk classification, and a lack of confidence reporting. We therefore set out to develop and test a machine learning algorithm that can overcome these limitations by accurately and confidently making predictions to support in-field triage in the first hours after traumatic injury. Methods Using an American College of Surgeons Trauma Quality Improvement Program-derived database of truncal and junctional gunshot wound (GSW) patients (aged 16-60 years), we trained an information-aware Dirichlet deep neural network (field artificial intelligence triage). Using supervised training, field artificial intelligence triage was trained to predict shock and the need for major hemorrhage control procedures or early massive transfusion (MT) using GSW anatomical locations, vital signs, and patient information available in the field. In parallel, a confidence model was developed to predict the true-class probability (scale of 0-1), indicating the likelihood that the prediction made was correct, based on the values and interconnectivity of input variables. Results A total of 29,816 patients met all the inclusion criteria. Shock, major surgery, and early MT were identified in 13.0%, 22.4%, and 6.3% of the included patients, respectively. Field artificial intelligence triage achieved mean areas under the receiver operating characteristic curve of 0.89, 0.86, and 0.82 for prediction of shock, early MT, and major surgery, respectively, for 80/20 train-test splits over 1,000 epochs. Mean predicted true-class probability for errors/correct predictions was 0.25/0.87 for shock, 0.30/0.81 for MT, and 0.24/0.69 for major surgery. Conclusion Field artificial intelligence triage accurately identifies potential shock in truncal GSW patients and predicts their need for MT and major surgery, with a high degree of certainty. The presented model is an important proof of concept. Future iterations will use an expansion of databases to refine and validate the model, further adding to its potential to improve triage in the field, both in civilian and military settings. Level of evidence Prognostic, Level III. |
| Databáze: | OpenAIRE |
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