Identifying trauma patients at risk of imminent hemorrhagic shock is a challenging task in intraoperative and battlefield settings given the variability of traditional vital signs, such as heart rate and blood pressure, and their inability to detect blood loss at an early stage. To this end, we acquired N = 58 photoplethysmographic (PPG) recordings from both trauma patients with suspected hemorrhage admitted to the hospital, and healthy volunteers subjected to blood withdrawal of 0.9 L. We propose four features to characterize each recording: goodness of fit (r2), the slope of the trend line, percentage change, and the absolute change between amplitude estimates in the heart rate frequency range at the first and last time points. Also, we propose a machine learning algorithm to distinguish between blood loss and no blood loss. The optimal overall accuracy of discriminating between hypovolemia and euvolemia was 88.38%, while sensitivity and specificity were 88.86% and 87.90%, respectively. In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn. The results suggest that the proposed features and algorithm are suitable for the automatic discrimination between hypovolemia and euvolemia, and can be beneficial and applicable in both intraoperative/emergency and combat casualty care.
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机译：考虑到传统生命体征（例如心率和血压）的可变性以及他们无法及早发现失血，要确定术中和战场环境中即将发生大出血性休克的外伤患者是一项艰巨的任务。为此，我们从入院的怀疑有出血的外伤患者和接受0.9 L采血的健康志愿者那里获得了N = 58的光电容积描记（PPG）记录。我们提出了四个特征来表征每个记录：拟合优度（ r 2 ），趋势线的斜率，百分比变化以及在第一个和最后一个时间点在心率频率范围内的幅度估计之间的绝对变化。此外，我们提出了一种机器学习算法，以区分失血量和无失血量。区分血容量不足和水肿程度的最佳总体准确度为88.38％，而敏感性和特异性分别为88.86％和87.90％。此外，即使抽出适量的血液，提出的功能和算法也能很好地执行。结果表明，所提出的特征和算法适用于血容量不足和血容量正常之间的自动判别，并且在术中/紧急情况和战斗伤亡护理中均可以是有益的和适用的。
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