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Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest

Moseby-Knappe, Marion LU ; Westhall, Erik LU ; Backman, Sofia LU ; Mattsson-Carlgren, Niklas LU ; Dragancea, Irina LU ; Lybeck, Anna LU ; Friberg, Hans LU ; Stammet, Pascal ; Lilja, Gisela LU and Horn, Janneke , et al. (2020) In Intensive Care Medicine 46(10). p.1852-1862
Abstract

Purpose: To assess the performance of a 4-step algorithm for neurological prognostication after cardiac arrest recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM). Methods: Retrospective descriptive analysis with data from the Target Temperature Management (TTM) Trial. Associations between predicted and actual neurological outcome were investigated for each step of the algorithm with results from clinical neurological examinations, neuroradiology (CT or MRI), neurophysiology (EEG and SSEP) and serum neuron-specific enolase. Patients examined with Glasgow Coma Scale Motor Score (GCS-M) on day 4 (72–96 h) post-arrest and available 6-month outcome were included. Poor outcome... (More)

Purpose: To assess the performance of a 4-step algorithm for neurological prognostication after cardiac arrest recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM). Methods: Retrospective descriptive analysis with data from the Target Temperature Management (TTM) Trial. Associations between predicted and actual neurological outcome were investigated for each step of the algorithm with results from clinical neurological examinations, neuroradiology (CT or MRI), neurophysiology (EEG and SSEP) and serum neuron-specific enolase. Patients examined with Glasgow Coma Scale Motor Score (GCS-M) on day 4 (72–96 h) post-arrest and available 6-month outcome were included. Poor outcome was defined as Cerebral Performance Category 3–5. Variations of the ERC/ESICM algorithm were explored within the same cohort. Results: The ERC/ESICM algorithm identified poor outcome patients with 38.7% sensitivity (95% CI 33.1–44.7) and 100% specificity (95% CI 98.8–100) in a cohort of 585 patients. An alternative cut-off for serum neuron-specific enolase, an alternative EEG-classification and variations of the GCS-M had minor effects on the sensitivity without causing false positive predictions. The highest overall sensitivity, 42.5% (95% CI 36.7–48.5), was achieved when prognosticating patients irrespective of GCS-M score, with 100% specificity (95% CI 98.8–100) remaining. Conclusion: The ERC/ESICM algorithm and all exploratory multimodal variations thereof investigated in this study predicted poor outcome without false positive predictions and with sensitivities 34.6–42.5%. Our results should be validated prospectively, preferably in patients where withdrawal of life-sustaining therapy is uncommon to exclude any confounding from self-fulfilling prophecies.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cardiac arrest, Coma, Guideline algorithm, Prognostic accuracy, Prognostication
in
Intensive Care Medicine
volume
46
issue
10
pages
1852 - 1862
publisher
Springer
external identifiers
  • pmid:32494928
  • scopus:85085936715
ISSN
0342-4642
DOI
10.1007/s00134-020-06080-9
language
English
LU publication?
yes
id
35d63923-2add-45f4-8b5f-ccdc3f7e341f
date added to LUP
2020-11-06 11:55:42
date last changed
2021-06-22 03:47:29
@article{35d63923-2add-45f4-8b5f-ccdc3f7e341f,
  abstract     = {<p>Purpose: To assess the performance of a 4-step algorithm for neurological prognostication after cardiac arrest recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM). Methods: Retrospective descriptive analysis with data from the Target Temperature Management (TTM) Trial. Associations between predicted and actual neurological outcome were investigated for each step of the algorithm with results from clinical neurological examinations, neuroradiology (CT or MRI), neurophysiology (EEG and SSEP) and serum neuron-specific enolase. Patients examined with Glasgow Coma Scale Motor Score (GCS-M) on day 4 (72–96 h) post-arrest and available 6-month outcome were included. Poor outcome was defined as Cerebral Performance Category 3–5. Variations of the ERC/ESICM algorithm were explored within the same cohort. Results: The ERC/ESICM algorithm identified poor outcome patients with 38.7% sensitivity (95% CI 33.1–44.7) and 100% specificity (95% CI 98.8–100) in a cohort of 585 patients. An alternative cut-off for serum neuron-specific enolase, an alternative EEG-classification and variations of the GCS-M had minor effects on the sensitivity without causing false positive predictions. The highest overall sensitivity, 42.5% (95% CI 36.7–48.5), was achieved when prognosticating patients irrespective of GCS-M score, with 100% specificity (95% CI 98.8–100) remaining. Conclusion: The ERC/ESICM algorithm and all exploratory multimodal variations thereof investigated in this study predicted poor outcome without false positive predictions and with sensitivities 34.6–42.5%. Our results should be validated prospectively, preferably in patients where withdrawal of life-sustaining therapy is uncommon to exclude any confounding from self-fulfilling prophecies.</p>},
  author       = {Moseby-Knappe, Marion and Westhall, Erik and Backman, Sofia and Mattsson-Carlgren, Niklas and Dragancea, Irina and Lybeck, Anna and Friberg, Hans and Stammet, Pascal and Lilja, Gisela and Horn, Janneke and Kjaergaard, Jesper and Rylander, Christian and Hassager, Christian and Ullén, Susann and Nielsen, Niklas and Cronberg, Tobias},
  issn         = {0342-4642},
  language     = {eng},
  number       = {10},
  pages        = {1852--1862},
  publisher    = {Springer},
  series       = {Intensive Care Medicine},
  title        = {Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest},
  url          = {http://dx.doi.org/10.1007/s00134-020-06080-9},
  doi          = {10.1007/s00134-020-06080-9},
  volume       = {46},
  year         = {2020},
}