Initial Development and Analysis of a Context-Aware Burn Resuscitation Decision-Support Algorithm

Burn patients require high-volume intravenous resuscitation with the goal of restoring global tissue perfusion to make up for burn-induced loss of fluid from the vasculature. Clinical standards of burn resuscitation are predominantly based on urinary output, which is not context-aware because it is not a trustworthy indicator of tissue perfusion. This paper investigates the initial development and analysis of a context-aware decision-support algorithm for burn resuscitation. In this context, we hypothesized that the use of a more context-aware surrogate of tissue perfusion may enhance the efficacy of burn resuscitation in normalizing cardiac output. Toward this goal, we exploited the arterial pulse wave analysis to discover novel surrogates of cardiac output. Then, we developed the cardiac output-enabled burn resuscitation decision-support (CaRD) algorithm. Using experimental data collected from animals undergoing burn injury and resuscitation, we conducted an initial evaluation and analysis of the CaRD algorithm in comparison with the commercially available Burn Navigator^TM algorithm. Combining a surrogate of cardiac output with urinary output in the CaRD algorithm has the potential to improve the efficacy of burn resuscitation. However, the improvement achieved in this work was only marginal, which is likely due to the suboptimal tuning of the CaRD algorithm with the limited available dataset. In this way, the results showed both promise and challenges that are crucial to future algorithm development.