Autonomous diagnosis and management of the critically ill during air transport (ADMIT)

航空运输中危重病人的自主诊断和管理（ADMIT）

• 批准号: 9912846
• 负责人: MICHAEL R PINSKY
• 金额: $ 76.14万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-10 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912846
• 关键词: Accident and Emergency department; Acute; Agreement; Air; Algorithms; Animals; Blood Pressure; Cardiovascular system; Caring; Classification; Clinical Treatment; Complex; Coupled; Critical Illness; Data; Data Set; Databases; Development; Diagnosis; Effectiveness; Electronic Health Record; Emergency Care; Emergency Department Physician; Environment; Family suidae; Frequencies; Graph; Health; Healthcare; Heart Rate; Hemorrhage; Hemorrhagic Shock; Hospitals; Human; Hypovolemia; Incidence; Inpatients; Intervention; Lead; Libraries; Link; Machine Learning; Measures; Mechanical ventilation; Medical; Melons; Modeling; Monitor; Morbidity - disease rate; Normal Range; Nurses; Organ failure; Paramedical Personnel; Pathologic Processes; Patient Monitoring; Patient-Focused Outcomes; Patients; Pattern; Physiological; Primary Health Care; Process; Protocols documentation; Records; Refractory; Resources; Resuscitation; Running; Sampling; Sepsis; Series; Serious Adverse Event; Severities; Shock; Site; Specificity; Standardization; System; Techniques; Testing; Time; Training; Trauma; Trauma patient; Traumatic Hemorrhage; Triage; Universities; Validation; Weaning; Work; advanced system; base; clinical care; clinical decision support; clinically relevant; cost; data modeling; data streams; demographics; diagnostic accuracy; effectiveness evaluation; hemodynamics; high risk; improved; in silico; indexing; insight; iterative design; mortality; non-invasive monitor; organ injury; patient population; predictive modeling; predictive tools; prospective; response; signal processing; simulation; success; support tools; tool; treatment response

Project Summary/Abstract: Cardiorespiratory instability (CRI) is common in trauma patients and other acutely ill patients being transferred from trauma sites or between hospital centers. Although paramedics/nurses (PM/RN) have some success in rescuing unstable patients with CRI using defined protocols and decrease incidence of inter-transport severe circulatory shock, the shock recognition tools available and resuscitation endpoints are limited to blood pressure and heart rate thresholds. However, CRI is often unrecognized until it is well established when patients are more refractory to treatment, or progressed to organ injury. If one could accurately predict who, when and why these critically ill patients develop CRI, then effective preemptive treatments could be given to improve care and triage resulting in better use of healthcare resources. We have shown that an integrated monitoring system alert obtained from continuous noninvasively acquired monitoring parameters coupled to a care algorithm improved step-down unit (SDU) patient outcomes. We also applied machine learning (ML) modeling to our clinically-relevant porcine model of hemorrhagic shock to characterize responses to hypovolemia, hemorrhage, and resuscitation, predict which animals would or would not collapse during hypovolemia, and identify occult bleeding 5 minutes earlier than with traditional monitoring. We now propose to apply our work to vulnerable STAT MedEvac air transported patients. We will validate these approaches in our existing >5,000 patient STAT MedEvac database, containing highly granular continuous non-invasive monitoring waveforms of air transported critically ill patients linked to their primary care and inpatient electronic health records (EHR). This level of patient information and granularity linked to treatment data and patient outcomes is unprecedented. We will extend our analysis to include more complex CRI, richer data, deeper analytics, and larger libraries of critically ill patients while in air transport, linking our proven Functional Hemodynamic Monitoring (FHM) principles for pathophysiologic diagnosis and resuscitation with non-invasive monitoring to operationalize personalized resuscitation. We will concurrently running two specific aims. First, we will develop through the Carnegie Melon University Auton Lab multivariable models through ML data-driven classification techniques to predict CRI. We will do this initially on our existing porcine hemorrhagic shock model data (n=60) and then on our STAT MedEvac dataset linked to EHR (n >5,000 patients), determining the minimal data (measures, sampling frequency, observation duration) required to robustly identify deviation from health, likely CRI cause, and response to treatment (endpoint of resuscitation), as well as the incremental benefit of additional variables, analysis, lead-time and sampling frequency to predict CRI and response to treatment, and examine the trade-offs between model parsimony and specificity. Second, we will evaluate our existing clinical decision support (CDS) tools to interface with FHM principles and ML- defined interactions, and trial this in silico first on our porcine hemorrhagic shock resuscitation, then on our STAT MedEvac data, followed by prospective human simulation on flight crew PM/RN (n=160) during annual training for agreement and benefit, defining effectiveness based on diagnosis accuracy, time to diagnosis, intervention choice accuracy and time to intervention. This iterative process will modify the existing CDS platform into one more specifically suited for air transport scenarios. Finally, we will evaluate the resultant semi-autonomous management protocol initially in retrospect in 100 STAT MedEvac patients and 10 Emergency Department trauma patients and then prospectively by active CDS in a final 100 STAT MedEvac patients. We will prospectively analyze the effectiveness of these calibrated CDS tools for predictive ability of the various ML models and apply the best, most practical and parsimonious predictive models for clinical care during transport based on patient population, pathological processes and support staff.

项目摘要/摘要：心肺不稳定（CRI）在创伤患者和其他 急性病患者从创伤部位或医院中心之间转移。虽然 护理人员/护士（PM/RN）在使用定义的CRI营救不稳定患者方 协议和降低交通间严重循环冲击的发生率，冲击识别工具 可用和复苏终点仅限于血压和心率阈值。但是，CRI是 当患者对治疗更难治或发展到 器官损伤。如果一个人可以准确预测这些重症患者发生CRI的谁，何时以及为什么 可以提供有效的先发制人治疗以改善护理和分类，从而更好地利用医疗保健 资源。我们已经表明，从连续无创获得的集成监视系统警报 获得的监测参数连接到护理算法改进的降级单元（SDU）患者结果。 我们还将机器学习（ML）建模应用于临床上与临床相关的出血性休克模型 为了表征对低血容量，出血和复苏的反应，预测哪些动物会或 低血容量期间不会崩溃，并比传统的人早5分钟识别出可疑的出血 监视。现在，我们建议将我们的工作应用于脆弱的Stat Medevac运输患者。我们将 在我们现有的> 5,000名患者Stat Medevac数据库中验证这些方法，其中包含高度细粒度 连续的非侵入性监测空气运输的危重患者的波形与其主要患者有关 护理和住院电子健康记录（EHR）。这种级别的患者信息和粒度与 治疗数据和患者预后是前所未有的。我们将扩展分析以包括更复杂的 在航空运输中，CRI，更丰富的数据，更深的分析和较大的重症患者图书馆 病理生理诊断和复苏的经过验证的功能性血液动力学监测（FHM）原理 通过非侵入性监测来操作个性化的复苏。我们将同时运行两个 具体目标。首先，我们将通过Carnegie Melon University Auton Lab多变量模型开发 通过ML数据驱动的分类技术来预测CRI。我们最初将在现有的猪上做到这一点 出血性休克模型数据（n = 60），然后在我们的STAT MEDEVAC数据集上链接到EHR（n> 5,000 患者），确定最小数据（度量，抽样频率，观察持续时间）需要 坚定地识别与健康的偏差，可能的CRI原因和对治疗的反应（复苏的终点）， 以及其他变量的增量益处，分析，销售时间和抽样频率以预测 CRI和对治疗的反应，并检查模型简约和特异性之间的权衡。第二， 我们将评估我们现有的临床决策支持（CDS）工具，以与FHM原理和ML-接口 定义的相互作用，首先在我们的猪出血性休克复苏中对此进行试验，然后在我们的 STAT MEDEVAC数据，然后在年度乘员PM/RN（n = 160）上进行前瞻性人类模拟 培训一致性和利益，根据诊断准确性，诊断时间定义有效性， 干预选择的准确性和干预时间。这个迭代过程将修改现有的CD 平台更适合一种适合航空运输方案的平台。最后，我们将评估结果 半自治管理方案最初是在100名STAT MEDEVAC患者和10个 急诊室创伤患者，然后在最后100个统计数据中通过活跃的CD进行前瞻性 患者。我们将前瞻性分析这些校准的CD工具的有效性，以预测能力 各种ML模型，并为临床护理应用最佳，最实用和简约的预测模型 在基于患者人群，病理过程和支持人员的运输过程中。