The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations.

Autor: Turella S; Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario 'Agostino Gemelli', IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy., Dankiewicz J; Department of Clinical Sciences Lund, Cardiology, Lund University, Lund, Sweden., Friberg H; Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Lund, Sweden., Jakobsen JC; Copenhagen Trial Unit, Capital Region, Copenhagen, Denmark.; Department of Regional Health Research, The Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark., Leithner C; Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany., Levin H; Department of Clinical Sciences Lund, Lund University, Lund, Sweden., Lilja G; Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.; Skane University Hospital, Lund, Sweden., Moseby-Knappe M; Department of Clinical Sciences Lund, Neurology and Rehabilitation, Lund University, Lund, Sweden., Nielsen N; Department of Clinical Sciences Lund, Anesthesiology and Intensive Care Medicine, Helsingborg Hospital, Helsingborg, Sweden., Rossetti AO; Department of Neurology, University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland., Sandroni C; Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario 'Agostino Gemelli', IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy., Zubler F; Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland., Cronberg T; Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden., Westhall E; Department of Clinical Sciences, Clinical Neurophysiology, Lund University, S-221 85, Lund, Sweden. erik.westhall@med.lu.se.
Jazyk: angličtina
Zdroj: Intensive care medicine [Intensive Care Med] 2024 Jan; Vol. 50 (1), pp. 90-102. Date of Electronic Publication: 2024 Jan 03.
DOI: 10.1007/s00134-023-07280-9
Abstrakt: Purpose: The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity.
Methods: This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA.
Results: 845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008).
Conclusion: The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.
(© 2024. The Author(s).)
Databáze: MEDLINE
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