A Case Study of AI-Enabled Software as a Medical Device Cleared by the FDA for Assessing Hemorrhage Risk Index (APPRAISE-HRI) after Trauma

Hemorrhage is the leading cause of preventable death on the battlefield, yet combat medics lack clinical decision support systems to help stratify hemorrhage risk in trauma casualties. We previously trained the Automated Processing of the Physiological Registry for Assessment of Injury Severity - Hemorrhage Risk Index (APPRAISE-HRI) software to associate patterns in vital signs (heart rate and blood pressure) collected from trauma patients with three HRI levels: I (low), II (average), or III (high). To independently validate APPRAISE-HRI and obtain U.S. Food and Drug Administration (FDA) clearance, we collected trauma registry and continuous vital sign data from 5895 trauma patients (543 with hemorrhagic injuries and 5352 controls) in an emergency department or during prehospital transport to one of eight medical centers. The study outcome was hemorrhagic injury, defined by documented injuries and blood transfusion. Using the likelihood ratio to assess the ability of APPRAISE-HRI to stratify hemorrhage risk, we found that hemorrhagic patients were 6.88 times as likely as controls to be at level III, strongly suggesting the presence of hemorrhage at this level. Similarly, hemorrhagic patients were 0.18 times as likely as controls to be at level I, suggesting the absence of hemorrhage at this level. Hemorrhagic patients were almost as likely as controls to be at level II (0.70 times as likely). Subsequently, the U.S. Department of Defense obtained FDA 510(k) clearance for the artificial intelligence-enabled APPRAISE-HRI Class II device (K233249), the first software as a medical device approved for assessing hemorrhage risk in trauma patients, allowing for triage and identification of casualties who need immediate attention and evacuation. (Funded by the U.S. Army Medical Materiel Development Activity and the Combat Casualty Care Program Area Directorate (CCCPAD) of the U.S. Army Medical Research and Development Command (USAMRDC), Fort Detrick, MD and others.).

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Publication Details

Journal
NEJM AI
Published
2025-10-16
DOI
https://doi.org/10.1056/aics2401170
Citations
3
Primary Topic
Trauma and Emergency Care Studies
Type
article
Field-Weighted Citation Impact
6.76

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article

A Case Study of AI-Enabled Software as a Medical Device Cleared by the FDA for Assessing Hemorrhage Risk Index (APPRAISE-HRI) after Trauma

Daniel G. Ostermayer, B SANDS, Gheorghe D. Doros, Mayur B. Patel et al.
3 citations
NEJM AI
Trauma and Emergency Care Studies
6.76
article

A Case Study of AI-Enabled Software as a Medical Device Cleared by the FDA for Assessing Hemorrhage Risk Index (APPRAISE-HRI) after Trauma

Daniel G. Ostermayer, B SANDS, Gheorghe D. Doros, Mayur B. Patel, Jaques Reifman, A V Prabhakaran, Andrew Frock, Suzanne Tamang, Ernest Eugene Moore, Francis Xavier Guyette, Andrew T. Reisner, Christopher Nemeth, Francisco G Vital-Lopez, David A. Spain, Gregory Rule, Stephen R. Wisniewski, Bellal A Joseph, J. Robbins, Jonathan D. Stallings, Brian G. Harbrecht, Bryan A. Cotton, Martin A. Schreiber, Jason L. Sperry, Chad T. Wilson, Valmik Desai, Sanjay Malunjkar
article en
3 citations

Abstract

Hemorrhage is the leading cause of preventable death on the battlefield, yet combat medics lack clinical decision support systems to help stratify hemorrhage risk in trauma casualties. We previously trained the Automated Processing of the Physiological Registry for Assessment of Injury Severity - Hemorrhage Risk Index (APPRAISE-HRI) software to associate patterns in vital signs (heart rate and blood pressure) collected from trauma patients with three HRI levels: I (low), II (average), or III (high). To independently validate APPRAISE-HRI and obtain U.S. Food and Drug Administration (FDA) clearance, we collected trauma registry and continuous vital sign data from 5895 trauma patients (543 with hemorrhagic injuries and 5352 controls) in an emergency department or during prehospital transport to one of eight medical centers. The study outcome was hemorrhagic injury, defined by documented injuries and blood transfusion. Using the likelihood ratio to assess the ability of APPRAISE-HRI to stratify hemorrhage risk, we found that hemorrhagic patients were 6.88 times as likely as controls to be at level III, strongly suggesting the presence of hemorrhage at this level. Similarly, hemorrhagic patients were 0.18 times as likely as controls to be at level I, suggesting the absence of hemorrhage at this level. Hemorrhagic patients were almost as likely as controls to be at level II (0.70 times as likely). Subsequently, the U.S. Department of Defense obtained FDA 510(k) clearance for the artificial intelligence-enabled APPRAISE-HRI Class II device (K233249), the first software as a medical device approved for assessing hemorrhage risk in trauma patients, allowing for triage and identification of casualties who need immediate attention and evacuation. (Funded by the U.S. Army Medical Materiel Development Activity and the Combat Casualty Care Program Area Directorate (CCCPAD) of the U.S. Army Medical Research and Development Command (USAMRDC), Fort Detrick, MD and others.).

NEJM AIVol. 2(11)
Henry M. Jackson Foundation (US), University of Arizona (US), University of Louisville (US), Oregon Health & Science University (US), University of Pittsburgh (US), Baylor College of Medicine (US), VA Palo Alto Health Care System (US), Defense Health Agency (US), Denver Health Medical Center (US), United States Army Medical Research and Development Command (US), Massachusetts General Hospital (US), Sci-Tech Daresbury (GB), Applied Research Associates (United States) (US), Vanderbilt University Medical Center (US), The University of Texas Health Science Center at Houston (US), University of Colorado Denver (US), Stanford University (US)
ZOLL Medical Corporation, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command
Openalex Percentile: Top 3%
Trauma and Emergency Care Studies
6.76
DOIOpen Access PDFOpenAlex
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