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

  • Big Data in Neurocritical Care
  • Published:
Neurocritical Care Aims and scope Submit manuscript

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.

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Funding

All study authors are supported on Grants from the NIH to Drs. Hirsch and Elmer for this work (Grant Nos. 5K23NS097629, 5R01NS119825).

Author information

Authors and Affiliations

  1. Departments of Emergency Medicine, Critical Care Medicine and Neurology, University of Pittsburgh, Iroquois Building, Suite 400A, 3600 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Jonathan Elmer

  2. Department of Neurology, Stanford University, Palo Alto, CA, USA

    Zihuai He, Elizabeth Osborn, Stephanie Kemp & Karen G. Hirsch

  3. Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA

    Zihuai He

  4. Department of Critical Care Services, Neuroscience Institute, Maine Medical Center, Portland, ME, USA

    Teresa May

  5. Moberg Analytics, Philadelphia, PA, USA

    Richard Moberg, Jesse Stover & Ethan Moyer

  6. Departments of Neurology, Anesthesiology-Critical Care Medicine and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Romergryko G. Geocadin

Authors
  1. Jonathan Elmer
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  2. Zihuai He
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  4. Elizabeth Osborn
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  5. Richard Moberg
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  6. Stephanie Kemp
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  7. Jesse Stover
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  8. Ethan Moyer
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  9. Romergryko G. Geocadin
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  10. Karen G. Hirsch
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Consortia

the PRECICECAP Study Team

Contributions

JE: substantial contributions to the conception and design of the work; drafted the article; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. ZH: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. TM: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. EO: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. RM: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. SK: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. JS: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. EM: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. RGG: substantial contributions to the conception and design of the work; critical revisions for important intellectual content; and agree to be accountable for the accuracy and integrity of the work. KGH: substantial contributions to the conception and design of the work; drafted the article; and agree to be accountable for the accuracy and integrity of the work. The final version of the manuscript was approved by all authors.

Corresponding author

Correspondence to Jonathan Elmer.

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Conflict of interest

Richard Moberg, Ethan Moyer, and Jesse Stover are employees of Moberg Analytics.

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Elmer, J., He, Z., May, T. et al. Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach. Neurocrit Care 37 (Suppl 2), 237–247 (2022). https://doi.org/10.1007/s12028-022-01464-9

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  • DOI: https://doi.org/10.1007/s12028-022-01464-9

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