Unifying the Estimation of Blood Volume Decompensation Status in a Porcine Model of Relative and Absolute Hypovolemia via Wearable Sensing.

Hypovolemia remains the leading cause of preventable death in trauma cases. Recent research has demonstrated that using noninvasive continuous waveforms rather than traditional vital signs improves accuracy in early detection of hypovolemia progression to assist in triage and resuscitation efforts. In this work, random forest models trained on different subsets of data from a pig model (n=6) of absolute (bleeding) and relative (nitroglycerin induced vasodilation) progressive hypovolemia (to 20% decrease in mean arterial pressure) and resuscitation are evaluated. Features for the models were derived from a multi-modal set of wearable sensors comprised of the electrocardiogram (ECG), seismocardiogram (SCG) and reflective photoplethysmogram (RPPG). The median RMSE between predicted and actual percent progression towards cardiovascular decompensation for this model was 30.5% during the relative period, 16.8% during absolute and 22.1% during resuscitation, with an overall median RMSE of 22.0%. The least squares best fit line over the mean aggregated predictions had a slope of 0.65 and intercept of 12.3, with an R2 value of 0.93. When transitioned to a binary classification problem to identify decompensation, this model achieved an area under the receiver operating characteristic curve of 0.80. This study provided the following advancements: a) developed a global model incorporating ECG, SCG and RPPG features for estimating individual-specific decompensation from progressive relative and absolute hypovolemia and resuscitation; b) demonstrated SCG as the most important modality to predict decompensation; c) demonstrated efficacy of random forest models trained on different data subsets; and d) demonstrated adding training data from two discrete forms of hypovolemia increases prediction accuracy for the other form of hypovolemia and resuscitation.