EMERGING TECHNOLOGY AND MODERNIZING THE ARMY
VITAL SIGNS
The first triage system ever cleared by the FDA for assessing hemorrhage risk in trauma patients is powered by AI. How did its inventors ensure it was up to the job?
by Paul Lagasse H
emorrhage, or uncontrolled blood loss, is the lead- ing cause of preventable death among combat casualties, representing approximately 90% of battlefield fatalities. Analysis of data from the
conflicts in Iraq and Afghanistan suggests that between 6% and 24% of those fatalities could potentially have survived if their injuries had been more quickly diagnosed and treated. Given that the median survival time from hemorrhage is just two-to-three hours, the window for identification and treatment is narrow.
Combat medics have long known that hemorrhage is associated with increased heart rate and decreased blood pressure. What they lacked, however, was a way to use that data to assess a trauma patient’s hemorrhage risk. A new application developed by the U.S. Army Medical Research and Development Command’s (MRDC) Biotechnology High Performance Computing Soft- ware Applications Institute (BHSAI) promises to fill that gap with the aid of artificial intelligence (AI).
Te Automated Processing of the Physiological Registry for Assessment of Injury Severity Hemorrhage Risk Index, or APPRAISE-HRI, is a smartphone health app that uses AI to quickly assess patients’ vital-sign data and assign them to one of three categories based on their risk of experiencing life-threaten- ing blood loss. In April, APPRAISE-HRI received the U.S. Food and Drug Administration’s (FDA) first-ever regulatory clearance of an application of its kind, also becoming the first AI-enabled software from the DOD ever cleared by the FDA. Te story of
how the BHSAI team collected and refined vital-sign data to train the AI algorithm at the core of the APPRAISE-HRI is a fascinat- ing tale of human-computer interaction that has valuable lessons for the future development of AI tools in military medicine.
COLLECTING AND ANALYZING VITAL-SIGN DATA In 2001, Jaques Reifman, Ph.D., director of BHSAI, first became interested in the problem of whether data collected from the continuous monitoring of vital signs might reveal trends that could be used diagnostically.
“It took time to determine which combination of vital signs we should be using,” said Reifman. “We attempted to develop models with all sorts of potential combinations, which was very time consuming. Ultimately, we used high performance comput- ing to identify systolic and diastolic blood pressure and heart rate as the most useful.”
Te challenge Reifman faced was determining how those vital signs correlated with a patient’s hemorrhage state. He proposed training a machine-learning algorithm on data collected from trauma patients to see what trends appeared. With support from the Defense Health Program, the Advanced Medical Technology Initiative, the Henry M. Jackson Foundation for the Advance- ment of Military Medicine and MRDC’s own Combat Casualty Care Research Program, Reifman and his team undertook three independent studies to collect vital-sign data from adult trauma
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