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

Artikel i vetenskaplig tidskrift
Författare Marion Moseby-Knappe
Erik Westhall
Sofia Backman
Niklas Mattsson-Carlgren
Irina Dragancea
Anna Lybeck
Hans Friberg
Pascal Stammet
Gisela Lilja
Janneke Horn
Jesper Kjaergaard
Christian Rylander
Christian Hassager
Susann Ullén
Niklas Nielsen
Tobias Cronberg
Publicerad i Intensive Care Medicine
ISSN 03424642
Publiceringsår 2020
Publicerad vid Institutionen för kliniska vetenskaper, Avdelningen för anestesiologi och intensivvård
Språk en
Ämnesord Cardiac arrest, Coma, Guideline algorithm, Prognostic accuracy, Prognostication
Ämneskategorier Kardiovaskulär medicin, Intensivvård

Sammanfattning

© 2020, The Author(s). 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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