Learning-Enabled Autonomous Decision-Support for Blood Pressure Management in Hemorrhage Resuscitation via Population-Informed Statistical Inference

项目概要/摘要 出血是全球约40%的创伤性损伤所致死亡的原因， 是1-46岁美国人死亡的主要原因。由于高比率的堕胎引起的死亡 在达到最终护理之前发生，为老年患者提供立即挽救生命的干预措施， 至关重要血压（BP）管理是出血的重要组成部分 复苏，因为它在（i）降低创伤诱导的死亡率以及（ii）发展 临床试验中的新型出血复苏方案。但是，临床医生不能有效地维持血压 在目标范围内，BP管理协议失败在临床试验中很常见。无论如何， 没有成熟的技术可供临床使用，以支持临床医生进行BP管理。 通过自主血管加压药给药指导技术， 目前正在FDA IDE下进行临床试验，研究小组建议开发一种学习- 在出血复苏期间启用自主决策支持（LEAD）系统进行BP管理， 其可以预测患者未来的血压并推荐复苏液体施用的时间和剂量 为了将患者的BP维持在临床医生指定的目标范围内，同时不断优化其 通过了解患者对液体给药的反应来提高准确性。LEAD系统将适用于 在ICU、ED甚至院前环境中的临床应用。LEAD系统在以下情况下最具影响力： 临床医生是新手、分心或疲劳。此外，通过保持临床医生在循环中，将有很多 降低监管风险，允许快速过渡到临床试验和传播。通过这种方式， 该系统有可能通过增强血液动力学， 临床医生对患者动态治疗轨迹的了解。 与LEAD系统有关的关键创新是：（一）一种新颖的人口知情、递归、集体 基于基于物理学生理学模型预测患者未来BP的统计推断方法 模型和集体推理开发的调查小组和（二）其现实世界的实施， 准备好用于临床使用的计算用户界面平台。为了实现和验证LEAD系统， 我们将（i）通过人口信息递归集体开发用于LEAD系统的BP预测算法 推断（SA 1）;（ii）使用临床数据集（SA 2）评估LEAD BP预测算法;以及（iii）实现 LEAD系统使用计算用户界面平台并进行模拟实时测试（SA 3）。 如果该项目成功，调查小组将着手进行技术商业化， 通过执行后续R 01提案，优化LEAD系统算法和用户界面， 平台，并在FDA IDE下进行临床试验。

PROJECT SUMMARY/ABSTRACT Hemorrhage is accountable for approximately 40% of deaths due to traumatic injuries worldwide as well as the leading cause of mortality in Americans 1-46 years of age. Since high rate of hemorrhage-induced deaths occur before reaching definitive care, providing immediate life-saving interventions to hemorrhaging patients is of paramount importance. Blood pressure (BP) management is a very important component of hemorrhage resuscitation due to its central role in (i) reducing the hemorrhage-induced mortality as well as in (ii) developing novel hemorrhage resuscitation protocols in clinical trials. But, clinicians are not effective at maintaining BP within a goal range, and BP management protocol failures are common in clinical trials. Regardless, there is no mature technology ready for clinical use to support clinicians with BP management. By extending its ongoing success with an autonomous vasopressor administration guidance technology currently undergoing a clinical trial under an FDA IDE, the investigative team proposes to develop a learning- enabled autonomous decision-support (LEAD) system for BP management during hemorrhage resuscitation, which can predict future BP in a patient and recommend timings and doses of resuscitation fluid administration in order to maintain the patient’s BP within a clinician-specified goal range, while continuously optimizing its accuracy by learning the patient’s response to administration of fluids. The LEAD system will be suitable for clinical use in ICUs, EDs, and even pre-hospital environments. The LEAD system will be most impactful when a clinician is novice, distracted, or tired. In addition, by maintaining clinicians in the loop, there will be much reduced regulatory risk, allowing for rapid transition to a clinical trial and dissemination. In this way, the LEAD system has the potential to enable tight BP management during hemorrhage resuscitation by enhancing the awareness of clinicians on a patient’s dynamic treatment trajectory. Key innovations pertaining to the LEAD system are (i) a novel population-informed, recursive, collective statistical inference approach to prediction of future BP in a patient based on a physics-based physiological model and a collective inference developed by the investigative team and (ii) its real-world implementation into a computational user interface platform being ready for clinical use. To realize and validate the LEAD system, we will (i) develop a BP prediction algorithm for the LEAD system via population-informed recursive collective inference (SA1); (ii) evaluate the LEAD BP prediction algorithm using clinical datasets (SA2); and (iii) realize the LEAD system using a computational user interface platform and conduct simulated real-time testing (SA3). If this project is successful, the investigative team will proceed to technology commercialization and translation by pursuing a follow-up R01 proposal to optimize the LEAD system algorithm and user interface platform, and conduct a clinical trial under an FDA IDE.