Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States of America.
Campbell University School of Medicine, Buies Creek, NC, United States of America.
PLoS One. 2018 Mar 29;13(3):e0195087. doi: 10.1371/journal.pone.0195087. eCollection 2018.
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 In addition, the proposed features and algorithm performed well even when moderate blood volume was withdrawn.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.
识别术中及战场环境下即将发生出血性休克的创伤患者是一项具有挑战性的任务，因为传统生命体征（如心率和血压）存在变异性，并且无法在早期检测到失血。为此，我们从怀疑有出血的住院创伤患者和接受 0.9 升血液抽取的健康志愿者中获得了 N = 58 个光体积描记（PPG）记录。我们提出了四个特征来描述每个记录：拟合度（r2）、趋势线斜率、百分比变化以及心率频带幅度估计值在第一和最后时间点之间的绝对变化。此外，我们提出了一种机器学习算法来区分失血和无失血。区分低血容量和正常血容量的最佳总体准确性为 88.38%，而灵敏度和特异性分别为 88.86%和 87.90%。此外，即使中等量的血液被抽出，所提出的特征和算法也能很好地工作。结果表明，所提出的特征和算法适用于自动区分低血容量和正常血容量，可以在术中/紧急情况和战斗伤员护理中受益和适用。
