Feature Importance Analysis for Compensatory Reserve to Predict Hemorrhagic Shock.

Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate on an arterial blood pressure (ABP) waveform acquired via photoplethysmography have been shown to provide an effective early indicator. However, these machine learning approaches lack physiological interpretability. In this paper, we evaluate the importance of nine ABP-derived features that provide physiological insight, using a database of 40 human subjects from a lower-body negative pressure model of progressive central hypovolemia. One feature was found to be considerably more important than any other. That feature, the half-rise to dicrotic notch (HRDN), measures an approximate time delay between the ABP ejected and reflected wave components. This delay is an indication of compensatory mechanisms such as reduced arterial compliance and vasoconstriction. For a scale of 0% to 100%, with 100% representing normovolemia and 0% representing decompensation, linear regression of the HRDN feature results in root-mean-squared error of 16.9%, R2 of 0.72, and an area under the receiver operating curve for detecting decompensation of 0.88. These results are comparable to previously reported results from the more complex black box machine learning models. Clinical Relevance- A single physiologically interpretable feature measured from an arterial blood pressure waveform is shown to be effective in monitoring for blood loss and impending hemorrhagic shock based on data from a human lower-body negative pressure model of progressive central hypolemia.