Zia Jonathan, Kimball Jacob, Rolfes Christopher, Hahn Jin-Oh, Inan Omer T
Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Emory University School of Medicine, Atlanta, GA 30322, USA.
Sci Adv. 2020 Jul 22;6(30):eabb1708. doi: 10.1126/sciadv.abb1708. eCollection 2020 Jul.
As the leading cause of trauma-related mortality, blood loss due to hemorrhage is notoriously difficult to triage and manage. To enable timely and appropriate care for patients with trauma, this work elucidates the externally measurable physiological features of exsanguination, which were used to develop a globalized model for assessing blood volume status (BVS) or the relative severity of blood loss. These features were captured via both a multimodal wearable system and a catheter-based reference and used to accurately infer BVS in a porcine model of hemorrhage ( = 6). Ultimately, high-level features of cardiomechanical function were shown to strongly predict progression toward cardiovascular collapse and used to estimate BVS with a median error of 15.17 and 18.17% for the catheter-based and wearable systems, respectively. Exploring the nexus of biomedical theory and practice, these findings lay the groundwork for digital biomarkers of hemorrhage severity and warrant further study in human subjects.
作为创伤相关死亡的主要原因,出血导致的失血极难进行分诊和处理。为了能够及时、适当地救治创伤患者,这项研究阐明了失血时可外部测量的生理特征,这些特征被用于开发一个评估血容量状态(BVS)或失血相对严重程度的全球化模型。这些特征通过多模态可穿戴系统和基于导管的参考装置进行采集,并用于在猪出血模型(n = 6)中准确推断BVS。最终,心脏机械功能的高级特征被证明能强烈预测心血管崩溃的进展,并分别用于估计基于导管系统和可穿戴系统的BVS,中位误差分别为15.17%和18.17%。通过探索生物医学理论与实践的联系,这些发现为出血严重程度的数字生物标志物奠定了基础,并值得在人类受试者中进一步研究。