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

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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.