Decision Support for Tactical Combat Casualty Care Using Machine Learning to Detect Shock. (25th January 2021)

• Record Type: Journal Article
• Authors: Nemeth, Christopher; Amos-Binks, Adam; Burris, Christie; Keeney, Natalie; Pinevich, Yuliya; Pickering, Brian W; Rule, Gregory; Laufersweiler, Dawn; Herasevich, Vitaly; Sun, Mei G
• Is Part Of: Military medicine. Volume 186(2021:Jan./Feb.)Supplement 1
• Page Start: 273
• Page End: 280
• Publication Date: 2021-01-25
• Subjects: Surgery, Military -- Societies, etc; Medicine, Military -- Societies, etc; Medicine, Military -- Periodicals; Surgery, Military -- Periodicals; Medicine, Military; Surgery, Military; Military Medicine -- Periodicals; Periodicals; Electronic journals; 616.98023
• DOI: 10.1093/milmed/usaa275
• Languages: English
• ISSNs: 0026-4075

Abstract

ABSTRACT: Introduction: The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers' ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation. Materials and Methods: Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews. Machine learning algorithm methods included development of a model trained on publicly available Medical Information Mart for Intensive Care data, then on de-identified data from Mayo Clinic Intensive Care Unit. Results: The project team interviewed 17 Army, Air Force, and Navy medical subject matter experts during design requirements review sessions. They had an average of 17 years of service in military medicine and an average of 4 deployments apiece and all had performed tactical combat casualty care on live patients during deployment. Comments provided requirements for shock identification and management in prehospital settings, including support for indication of shock probability and shock differentiation. The machine learning algorithm based on logistic regression performed best among other algorithms we tested and was able to predict shock onset 90 minutes before it occurred with better than 75% accuracy in the test dataset. Conclusions: We expect the Trauma Triage, Treatment, and Training Decision Support system will augment a medic's ability to make informed decisions based on salient patient data and to diagnose multiple types of shock through remotely trained, field deployed ML models.