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

项目摘要/摘要：心肺不稳定 (CRI) 在创伤患者和其他患者中很常见 从创伤地点或医院中心之间转移的急症患者。虽然 护理人员/护士 (PM/RN) 使用定义的方法在抢救不稳定的 CRI 患者方面取得了一些成功 协议并减少转运期间严重循环性休克的发生率，休克识别工具 可用的复苏终点仅限于血压和心率阈值。然而，CRI 通常在患者对治疗更加难治或进展到病情明确时才被识别出来 器官损伤。如果能够准确预测这些危重患者是谁、何时以及为什么会出现 CRI，那么 可以采取有效的先发性治疗来改善护理和分诊，从而更好地利用医疗保健 资源。我们已经证明，通过连续的无创性监测获得的综合监测系统警报 获得的监测参数与护理算法相结合，改善了降压单元 (SDU) 患者的治疗效果。 我们还将机器学习 (ML) 建模应用于临床相关的失血性休克猪模型 描述对低血容量、出血和复苏的反应，预测哪些动物会或 低血容量时不会崩溃，比传统方法提前 5 分钟识别隐匿性出血 监控。我们现在建议将我们的工作应用于易受伤害的 STAT MedEvac 空运患者。我们会 在我们现有的超过 5,000 名患者 STAT MedEvac 数据库中验证这些方法，其中包含高度精细的数据 对空运的危重患者与其主要疾病相关的连续无创监测波形 护理和住院患者电子健康记录 (EHR)。这种级别的患者信息和粒度与 治疗数据和患者结果是前所未有的。我们将扩展我们的分析以包括更复杂的 CRI、更丰富的数据、更深入的分析以及航空运输中更大的危重患者库，将我们的 用于病理生理诊断和复苏的功能性血流动力学监测 (FHM) 原理已得到验证 通过无创监测来实施个性化复苏。我们将同时运行两个 具体目标。首先，我们将通过卡内基梅隆大学Auton Lab开发多变量模型 通过 ML 数据驱动的分类技术来预测 CRI。我们首先将在现有的猪身上进行此操作 失血性休克模型数据 (n=60)，然后是链接到 EHR 的 STAT MedEvac 数据集 (n >5,000 患者），确定所需的最少数据（测量、采样频率、观察持续时间） 稳健地识别健康偏差、可能的 CRI 原因以及对治疗的反应（复苏终点）， 以及用于预测的附加变量、分析、交付周期和采样频率的增量效益 CRI 和治疗反应，并检查模型简约性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 原则和 ML 相结合 定义的相互作用，并首先在我们的猪失血性休克复苏中进行计算机试验，然后在我们的 STAT MedEvac 数据，然后在年度期间对飞行机组 PM/RN (n=160) 进行前瞻性人体模拟 培训以达成一致和利益，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CDS 平台成为一个更适合航空运输场景的平台。最后，我们将评估结果 半自主管理方案最初在 100 名 STAT MedEvac 患者和 10 名患者中进行回顾 急诊科创伤患者，然后在最后 100 名 STAT MedEvac 中前瞻性地接受主动 CDS 患者。我们将前瞻性地分析这些经过校准的 CDS 工具的预测能力的有效性 各种机器学习模型，并将最好、最实用、最简约的预测模型应用于临床护理 在运输过程中，根据患者人数、病理过程和支持人员而定。

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.