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

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $652,539

## Abstract

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 sp...

## Key facts

- **NIH application ID:** 10141287
- **Project number:** 5R01HL141916-03
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** MICHAEL R PINSKY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $652,539
- **Award type:** 5
- **Project period:** 2019-04-10 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10141287

## Citation

> US National Institutes of Health, RePORTER application 10141287, Autonomous diagnosis and management of the critically ill during air transport (ADMIT) (5R01HL141916-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10141287. Licensed CC0.

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