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

项目摘要/摘要：心脏呼吸不稳定(CRI)在创伤患者和其他患者中很常见 急病患者从创伤现场或在医院中心之间转移。虽然 护理人员/护士(PM/RN)在抢救患有CRI的不稳定患者方面取得了一些成功，使用Defined 休克识别工具--传输间严重循环性休克的治疗方案和减少发生率 可用和复苏终点仅限于血压和心率阈值。然而，CRI是 通常不被发现，直到患者对治疗更难治或进展到 器官损伤。如果一个人能够准确地预测这些危重患者发生CRI的人、时间和原因，那么 可以给予有效的先发制人的治疗，以改善护理和分类，从而更好地利用医疗保健 资源。我们已经证明，从连续非侵入性获得的综合监控系统警报 获得的监测参数与护理算法相结合，改善了降压单元(SDU)患者的结果。 我们还将机器学习(ML)建模应用于我们的临床相关的失血性休克猪模型 为了表征对低血容量、出血和复苏的反应，预测哪些动物或 在低血容量期间不会崩溃，并比传统方法提前5分钟识别出隐性出血 监控。我们现在建议将我们的工作应用于脆弱的STAT医疗救护中心空运的患者。我们会 在我们现有的&>5,000名患者统计医疗救护中心数据库中验证这些方法，该数据库包含高度细粒度 空运危重病患者与其原发病相关的连续无创监测波形 护理和住院电子健康记录(EHR)。这一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，以包括更复杂的内容 CRI，更丰富的数据，更深入的分析，以及更大的危重患者信息库，将我们的 用于病理生理诊断和复苏的经验证的功能血流动力学监测(FHM)原理 使用非侵入性监测来操作个性化复苏。我们将同时运行两个 明确的目标。首先，我们将通过卡内基甜瓜大学奥顿实验室开发多变量模型 通过ML数据驱动分类技术对CRI进行预测。我们将首先在我们现有的猪身上执行此操作 失血性休克模型数据(n=60)，然后在我们的STAT医疗救护数据集中链接到EHR(n&>；5,000 患者)，确定所需的最小数据(测量、采样频率、观察持续时间 坚定地确定偏离健康、可能的CRI原因和对治疗的反应(复苏终点)； 以及额外变量、分析、提前期和采样频率的增量收益来预测 CRI和对治疗的反应，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- 定义了相互作用，并首先在我们的猪失血性休克复苏上进行了硅胶试验，然后在我们的 统计医疗救护数据，随后对年度期间的机组人员PM/RN(n=160)进行预期的人体模拟 培训的一致性和益处，基于诊断准确性、诊断时间、 干预选择的准确性和干预的时间。此迭代过程将修改现有的CDS 平台变成了一个更适合航空运输场景的平台。最后，我们将对结果进行评估 100例急救病人和10例急救病人半自主管理方案的初步回顾 急诊科创伤患者，然后预期在最后100个统计的医疗救护中由活跃的CDS 病人。我们将前瞻性地分析这些校准的CDS工具对 各种ML模型，并将最好、最实用和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。

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.