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

通过基于人群的统计推断，为出血复苏中的血压管理提供学习型自主决策支持

• 批准号: 10727737
• 负责人: Jin-Oh Hahn
• 金额: $ 33.5万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10727737
• 关键词: Accountability; Age Years; Algorithms; American; Automated Clinical Decision Support; Awareness; Blood Pressure; Caring; Cessation of life; Clinical; Clinical Data; Clinical Trials; Data Set; Decision Support Systems; Development; Dose; Environment; Failure; Fatality rate; Future; Goals; Hemorrhage; Hospitals; Intervention; Learning; Life; Liquid substance; Medical Device; Operative Surgical Procedures; Patients; Physics; Population; Pre-hospital setting; Protocols documentation; Recommendation; Reporting; Resuscitation; Risk; Role; Specific qualifier value; System; Technology; Testing; Time; Traffic accidents; Translations; Traumatic injury; Vasoconstrictor Agents; Violence; Work; blood pressure control; commercialization; experience; follow-up; innovation; learning algorithm; mortality; novel; patient response; physiologic model; prediction algorithm; prevent; response; success

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.

项目摘要/摘要 由于全球创伤性伤害以及 1-46岁的美国人死亡的主要原因。由于出血诱发的死亡率高 在获得明确的护理之前发生，为出血患者提供立即挽救生命的干预措施是 至关重要。血压（BP）管理是出血的非常重要的组成部分 复苏由于其在（i）降低出血诱导的死亡率以及（ii）发展中的核心作用而引起的。 临床试验中的新型出血复苏方案。但是，临床医生在维持BP方面无效 在目标范围内，BP管理方案失败在临床试验中很常见。无论如何，有 没有成熟的技术准备临床用来支持BP管理的临床医生。 通过使用自动加压器管理指导技术扩大其持续的成功 目前，正在接受FDA IDE的临床试验，调查团队的提议要开发学习 - 在出血复苏期间启用了自主决策支持（铅）系统，用于BP管理， 可以预测患者的未来BP，并建议使用时间和剂量的复苏液给药 为了将患者的BP保持在临床指定目标范围内，同时继续优化其 通过学习患者对流体给药的反应来精确。铅系统适合 ICU，ED甚至院前环境中的临床用途。铅系统将在 临床是新颖，分心或疲倦的。此外，通过将临床医生维持在循环中，将会有很多 降低了监管风险，从而可以快速过渡到临床试验和传播。这样，领先 系统有可能通过增强出血复苏期间的严格的BP管理 意识到临床医生对患者动态治疗轨迹的认识。 与铅系统有关的关键创新是（i）一种新颖的人口信息，递归，集体 基于物理生理学的患者预测患者未来BP的统计推断方法 模型和由调查团队开发的集体推论以及（ii）现实世界实施 一个计算用户界面平台已准备好供临床使用。实现和验证铅系统， 我们将（i）通过人群信息集体为铅系统开发BP预测算法 推理（SA1）; （ii）使用临床数据集（SA2）评估铅BP预测算法； （iii）意识到 使用计算用户界面平台并进行模拟的实时测试（SA3）的铅系统。 如果该项目成功，调查团队将继续进行技术商业化和 通过追求后续R01提案进行翻译，以优化铅系统算法和用户界面 平台，并根据FDA IDE进行临床试验。