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LAMMP Seminar Video
Triage Decision Support on The Battlefield: A Strategy for Advanced Monitoring of Combat Casualties
Victor A. Convertino, Ph.D., FACSM, FAsMA

Hemorrhage continues to be the leading preventable cause of mortality in both military and civilian trauma, accounting for more than 80% of military combat-related deaths classified as potentially survivable. Most pre-hospital medical interventions and treatments during civilian and military trauma casualty transport fail to utilize advanced decision-support systems. The initiation of treatment is usually based on standard vital signs (e.g., blood pressure, arterial oxygen saturation) which have proven to be of limited value in detecting the need to implement an intervention prior to hemodynamic decompensation, when hemorrhagic shock may be irreversible. A primary objective of the US Army combat casualty care research program is to reduce mortality and morbidity at the site of injury, during transport from the battlefield, and at higher echelons of care through advanced development of semi-automated decision-support capabilities for triage and autonomous care. To accomplish this goal, the Trauma Informatics research team at the US Army Institute of Surgical Research has used two approaches for evidence-based decision support: 1) a trauma patient database for capture and analysis of pre-hospital vital signs for identification of early, novel physiologic measurements that could improve the prediction of outcome and the control of closed-loop systems in trauma patients; and, 2) a human experimental model of reduced central blood volume using lower body negative pressure (LBNP) to improve the understanding and identification of novel physiological signals for advancing early diagnosis and closed-loop capabilities with hemodynamic, autonomic and metabolic responses to simulated hemorrhage. Using the trauma patient database, we have demonstrated that traditional vital sign measurements taken from standard medical monitors such as systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), arterial oxygen saturation (SpO2), end-tidal CO2, heart rate (HR) and respiration rate fail to reliably predict mortality or indicate the need for life saving interventions (LSIs) or blood loss until after the onset of cardiovascular collapse or circulatory shock. Likewise, standard physical examination methods used by medics such as pulse character and onset of symptoms do not always provide early indication of injury severity until just prior to cardiovascular collapse. Our laboratory experiments using LBNP have verified the results obtained from trauma patients and extended our insight to provide new evidence that early indicators of reduced central blood volume in the presence of stable vital signs include reductions in stroke volume and pulse pressure, increased arterial blood pressure oscillations, reductions in tissue oxygenation and changes in the morphology of photoplethysmographic signals. Based on our laboratory testing of new technologies, we are currently developing real-time, machine-learning algorithms that could improve the sensitivity and specificity of decision-support for diagnosis and treatment (e.g., closed-loop resuscitation and oxygen delivery) by providing earlier indications of clinical status during the pre-shock stage of hemorrhage. These technological advances could prove instrumental in advancing decision-support capabilities for pre-hospital trauma care at the site of injury and during transport to higher levels of care in both the military and civilian environments.

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