Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach

Authors

Jonathan Elmer, Departments of Emergency Medicine, Critical Care Medicine and Neurology, University of Pittsburgh, Iroquois Building, Suite 400A, 3600 Forbes Avenue, Pittsburgh, PA, 15213, USA. elmerjp@upmc.edu.
Zihuai He, Department of Neurology, Stanford University, Palo Alto, CA, USA.
Teresa May, Department of Critical Care Services, Neuroscience Institute, Maine Medical Center, Portland, ME, USA.
Elizabeth Osborn, Department of Neurology, Stanford University, Palo Alto, CA, USA.
Richard Moberg, Moberg Analytics, Philadelphia, PA, USA.
Stephanie Kemp, Department of Neurology, Stanford University, Palo Alto, CA, USA.
Jesse Stover, Moberg Analytics, Philadelphia, PA, USA.
Ethan Moyer, Moberg Analytics, Philadelphia, PA, USA.
Romergryko G. Geocadin, Departments of Neurology, Anesthesiology-Critical Care Medicine and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Karen G. Hirsch, Department of Neurology, Stanford University, Palo Alto, CA, USA.

Document Type

Article

Publication Date

3-1-2022

Institution/Department

Critical Care Medicine

Journal Title

Neurocritical care

Abstract

BACKGROUND: Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications. METHODS: The Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study is a response-adaptive, dose-finding trial testing different durations of therapeutic hypothermia. Twelve ICECAP sites will collect data for PRECICECAP from multiple modalities routinely used after out-of-hospital cardiac arrest, including ICECAP case report forms, detailed medication data, cardiopulmonary and electroencephalographic waveforms, and digital imaging and communications in medicine files (DICOMs). We partnered with Moberg Analytics to develop a freely available software platform to allow high-resolution critical care data to be used efficiently and effectively. We will use an autoencoder neural network to create low-dimensional representations of all raw waveforms and derivative features, censored at rewarming to ensure clinical usability to guide optimal duration of hypothermia. We will also consider simple features that are historically considered to be important. Finally, we will create a supervised deep learning neural network algorithm to directly predict 90-day functional outcome from large sets of novel features. RESULTS: PRECICECAP is currently enrolling and will be completed in late 2025. CONCLUSIONS: Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.

Recommended Citation

Elmer J, He Z, May T, et al. Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach [published online ahead of print, 2022 Mar 1]. Neurocrit Care. 2022;10.1007/s12028-022-01464-9. doi:10.1007/s12028-022-01464-9