Lightweight optoimpedance sensors enabling early detection of the physiological response to injury and illness

摘要 在休克发作前早期检测持续出血（OH）是公认的巨大未满足需求， 尤其是在创伤后。OH检测的延迟与“抢救失败”和 一旦发生临床上明显的休克，预后就会急剧恶化。虽然单一无创 技术未能检测或诊断复杂的疾病状态，我们已经证明了 计算机模拟的多重方法。这个STTR项目的目标是开发一个商业上可行的光阻抗 基于传感器的系统，结合了最先进的非侵入式传感技术和先进的多变量 统计算法第一阶段包括三个目标：1）设计、制作和测试光阻抗器件 传感器，2）在安全云上开发移动的应用程序、数据和ML管道，以及3）在 未麻醉直立猪出血模型。通过降低硬件挑战的风险， 在第二阶段应用中处于有利地位，可优化oPiic设计和制造， 目前正在国防部的支持下开发的算法，并在重症监护环境中进行临床试验验证。

Abstract Early detection of ongoing hemorrhage (OH) before onset of shock is a universally acknowledged great unmet need, and particularly important after trauma. Delays in the detection of OH are associated with a “failure to rescue” and a dramatic deterioration in prognosis once the onset of clinically frank shock has occurred. While uniplex noninvasive technologies have failed to detect or diagnose complex disease states, we have demonstrated the superiority of multiplex approaches in silico. The goal of this STTR project is to develop a commercially viable optoimpedance sensor-based system that combines state-of-the-art noninvasive sensing technologies and advanced multivariable statistical algorithms. Phase I will involve three Aims: 1) D​esign, Fabricate and Test Opto-Impedance oPiic sensors, 2) Develop of Mobile App, Data and ML Pipeline on Secure Cloud, and 3) Evaluate oPiics on an Unanesthetized Upright Porcine Hemorrhage Model. By derisking the hardware challenges, we will be well-positioned for a Phase II application to optimize oPiic design and manufacturing, fold-in predictive algorithms under current development with DOD support, and validate with a clinical trial in critical care setting.