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

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          Abstract

          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.

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          Author and article information

          Journal
          Neurocrit Care
          Neurocritical care
          Springer Science and Business Media LLC
          1556-0961
          1541-6933
          Aug 2022
          : 37
          : Suppl 2
          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. elmerjp@upmc.edu.
          [2 ] Department of Neurology, Stanford University, Palo Alto, CA, USA.
          [3 ] Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA.
          [4 ] Department of Critical Care Services, Neuroscience Institute, Maine Medical Center, Portland, ME, USA.
          [5 ] Moberg Analytics, Philadelphia, PA, USA.
          [6 ] Departments of Neurology, Anesthesiology-Critical Care Medicine and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
          Article
          NIHMS1888651 10.1007/s12028-022-01464-9
          10.1007/s12028-022-01464-9
          10134774
          35229231
          5252728a-3348-4165-bfc4-862af3cc0419
          History

          Outcome,Brain injury,Cardiac arrest,Machine learning,Precision medicine,Prognosis

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