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Nyholm B, Grand J, Obling LER, Hassager C, Møller JE, Schmidt H, Othman MH, Kondziella D, Kjaergaard J. Quantitative pupillometry for neuroprognostication in comatose post-cardiac arrest patients: A protocol for a predefined sub-study of the Blood pressure and Oxygenations Targets after Out-of-Hospital Cardiac Arrest (BOX)-trial. Resusc Plus 2023; 16:100475. [PMID: 37779885 PMCID: PMC10540039 DOI: 10.1016/j.resplu.2023.100475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2023] Open
Abstract
Background Resuscitation guidelines propose a multimodal prognostication strategy algorithm at ≥72 hours after the return of spontaneous circulation to evaluate neurological outcome for unconscious cardiac arrest survivors. Even though guidelines suggest quantitative pupillometry for assessing pupillary light reflex, threshold values are not yet validated.This study aims to validate pre-specified thresholds of quantitative pupillometry by quantitatively assessing the percentage reduction of pupillary size (qPLR) <4% and Neurological Pupil index (NPi) ≤2 and in predicting unfavorable neurological outcome. Both as an isolated predictor and combined with guideline-suggested neuron-specific enolase (NSE) threshold >60 μg L-1 in the current prognostication strategy algorithm. Methods We conduct this pre-planned diagnostic sub-study in the randomized, controlled, multicenter clinical trial "Blood Pressure and Oxygenation Targets after Out-of-Hospital Cardiac Arrest-trial". Blinded to treating physicians and outcome assessors, measurements of qPLR and NPi are obtained from cardiac arrest survivors at time points (±6 hours) of admission, after 24, 48, and 72 hours, or until the time of awakening or death. Discussion This study will be the largest prospective study investigating the predictive performance of automated quantitative pupillometry in unconscious patients resuscitated from cardiac arrest. We will test specific threshold values of NPi ≤2 and qPLR <4% to predict unfavorable outcome following cardiac arrest. The validation of pupillometry alone and combined with NSE with the criteria of the current prognostication strategy algorithm will hopefully increase the level of evidence and support clinical neuroprognostication with automated quantitative pupillometry in unconscious post-cardiac arrest patients. Trial registration Registered March 30, 2017, at ClinicalTrials.gov (Identifier: NCT03141099).
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Affiliation(s)
- Benjamin Nyholm
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Johannes Grand
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Christian Hassager
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jacob Eifer Møller
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Department of Cardiology, Odense University Hospital, 5000 C Odense, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Henrik Schmidt
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Anesthesiology and Intensive Care, Odense University Hospital, Odense, Denmark
| | - Marwan H. Othman
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Daniel Kondziella
- Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jesper Kjaergaard
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
- Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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