Steven L Moulton1, Jane Mulligan, Greg Z Grudic, Victor A Convertino. 1. From the Department of Surgery (S.L.M.), University of Colorado, School of Medicine, Aurora; and Flashback Technologies Inc. (S.L.M., J.M., G.Z.G.), Boulder, Colorado; and US Army Institute of Surgical Research (V.A.C.), JBSA Fort Sam Houston, San Antonio, Texas.
Abstract
BACKGROUND: Hemorrhage is a leading cause of traumatic death. We hypothesized that state-of-the-art feature extraction and machine learning techniques could be used to discover, detect, and continuously trend beat-to-beat changes in arterial pulse waveforms associated with the progression to hemodynamic decompensation. METHODS: We exposed 184 healthy humans to progressive central hypovolemia using lower-body negative pressure to the point of hemodynamic decompensation (systolic blood pressure > 80 mm Hg with or without bradycardia). Initial models were developed using continuous noninvasive blood pressure waveform data. The resulting algorithm calculates a compensatory reserve index (CRI), where 1 represents supine normovolemia and 0 represents the circulatory volume at which hemodynamic decompensation occurs (i.e., "running on empty"). Values between 1 and 0 indicate the proportion of reserve remaining before hemodynamic decompensation-much like the fuel gauge of a car indicates the amount of fuel remaining in the tank. A CRI estimate is produced after the first 30 heart beats, followed by a new CRI estimate after each subsequent beat. RESULTS: The CRI model with a 30-beat window has an absolute difference between actual and expected time to decompensation of 0.1, with a SD of 0.09. The model distinguishes individuals with low tolerance to reduced central blood volume (i.e., those most likely to develop early shock) from those with high tolerance and are able to estimate how near or far an individual may be from hemodynamic decompensation. CONCLUSION: Machine modeling can quickly and accurately detect and trend central blood volume reduction in real time during the compensatory phase of hemorrhage as well as estimate when an individual is "running on empty" and will decompensate (CRI, 0), well in advance of meaningful changes in traditional vital signs.
BACKGROUND:Hemorrhage is a leading cause of traumatic death. We hypothesized that state-of-the-art feature extraction and machine learning techniques could be used to discover, detect, and continuously trend beat-to-beat changes in arterial pulse waveforms associated with the progression to hemodynamic decompensation. METHODS: We exposed 184 healthy humans to progressive central hypovolemia using lower-body negative pressure to the point of hemodynamic decompensation (systolic blood pressure > 80 mm Hg with or without bradycardia). Initial models were developed using continuous noninvasive blood pressure waveform data. The resulting algorithm calculates a compensatory reserve index (CRI), where 1 represents supine normovolemia and 0 represents the circulatory volume at which hemodynamic decompensation occurs (i.e., "running on empty"). Values between 1 and 0 indicate the proportion of reserve remaining before hemodynamic decompensation-much like the fuel gauge of a car indicates the amount of fuel remaining in the tank. A CRI estimate is produced after the first 30 heart beats, followed by a new CRI estimate after each subsequent beat. RESULTS: The CRI model with a 30-beat window has an absolute difference between actual and expected time to decompensation of 0.1, with a SD of 0.09. The model distinguishes individuals with low tolerance to reduced central blood volume (i.e., those most likely to develop early shock) from those with high tolerance and are able to estimate how near or far an individual may be from hemodynamic decompensation. CONCLUSION: Machine modeling can quickly and accurately detect and trend central blood volume reduction in real time during the compensatory phase of hemorrhage as well as estimate when an individual is "running on empty" and will decompensate (CRI, 0), well in advance of meaningful changes in traditional vital signs.
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