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.

项目总结/文摘