Project Details
Description
Project Summary/Abstract
Out-of-hospital cardiac arrest (OHCA) is a dynamic process that requires new interventions to improve
outcomes. End-tidal carbon dioxide (ETCO2) measurement is a tool that is widely recognized, easy to use, and
can potentially provide real-time insights into ongoing resuscitation efforts; however, it has yet to be applied to
individualized medicine. Our overall hypothesis is that integrating ETCO2 capnography into OHCA resuscitation
will improve outcomes. Using innovative signal processing and machine learning methods, we will identify a wide
range of resuscitation quality characteristics over resuscitation, their relation to individual patient characteristics
and predictability of OHCA outcomes.
These goals will be accomplished via the following aims:
Aim 1. Determine the influence of resuscitation interventions on real-time physiologic dynamics and outcomes
in OHCA.
Aim 2. Establish the influence of individual patient characteristics on the real-time physiologic dynamics and
OHCA outcomes.
Aim 3. Develop a novel cardiac arrest resuscitation strategy based upon real-time individualized physiologic
dynamics.
We will create a large repository of cardiopulmonary resuscitation process data encompassing data from over
5300 adult OHCA. This work will define intra-arrest ETCO2 dynamics over resuscitation to allow for the
development of guided resuscitation efforts, and the resultant data will provide a solid foundation for future
hypothesis-driven research.
Dr. Nassal’s training plan encompasses both formal didactics and experiential training with experienced
mentors and collaborators that will develop a skillset in both signal processing and equitable artificial intelligent
driven algorithms. The team has extensive experience in using machine learning and multimodal signal
processing for classification and predictions in resuscitation. This training program will develop a unique skillset
in advanced cardiac signal processing; artificial intelligence, including equitable machine learning processing;
and expertise in the application of these skills to develop dynamically guided resuscitation strategies that few
other physician-scientist possess.
Status | Active |
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Effective start/end date | 09/1/23 → 08/31/24 |
Funding
- National Heart, Lung, and Blood Institute: $163,512.00
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