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

Sara Turella, Josef Dankiewicz, Hans Friberg, Janus Christian Jakobsen, Christoph Leithner, Helena Levin, Gisela Lilja, Marion Moseby-Knappe, Niklas Nielsen, Andrea O. Rossetti, Claudio Sandroni, Frédéric Zubler, Tobias Cronberg, Erik Westhall

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review


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.

TidskriftIntensive Care Medicine
StatusE-pub ahead of print - 2024

Ämnesklassifikation (UKÄ)

  • Anestesi och intensivvård
  • Neurologi


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