Filippo Bongiovanni1,2, Federico Romagnosi1,3, Giuseppina Barbella4,5, Arianna Di Rocco6, Andrea O Rossetti4, Fabio Silvio Taccone7, Claudio Sandroni2, Mauro Oddo8. 1. Department of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland. 2. Department of Anesthesiology and Intensive Care Medicine, Catholic University of The Sacred Heart, Rome, Italy. 3. Section of Anaesthesiology and Intensive Care, Department of Surgery, Dentistry, Paediatrics and Gynaecology, University Hospital Integrated Trust of Verona, Verona, Italy. 4. Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland. 5. Neurology Unit, San Gerardo Hospital, School of Medicine and Surgery and Milan-Center for Neuroscience (NeuroMI), University of Milano-Bicocca, Milan, Italy. 6. Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy. 7. Department of Intensive Care Medicine, Erasme University Hospital, Brussels, Belgium. 8. Department of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland. Mauro.oddo@chuv.ch.
Abstract
PURPOSE: Post-resuscitation guidelines recommend a multimodal algorithm for outcome prediction after cardiac arrest (CA). We aimed at evaluating the prevalence of indeterminate prognosis after application of this algorithm and providing a strategy for improving prognostication in this population. METHODS: We examined a prospective cohort of comatose CA patients (n = 485) in whom the ERC/ESICM algorithm was applied. In patients with an indeterminate outcome, prognostication was investigated using standardized EEG classification (benign, malignant, highly malignant) and serum neuron-specific enolase (NSE). Neurological recovery at 3 months was dichotomized as good (Cerebral Performance Categories [CPC] 1-2) vs. poor (CPC 3-5). RESULTS: Using the ERC/ESICM algorithm, 155 (32%) patients were prognosticated with poor outcome; all died at 3 months. Among the remaining 330 (68%) patients with an indeterminate outcome, the majority (212/330; 64%) showed good recovery. In this patient subgroup, absence of a highly malignant EEG by day 3 had 99.5 [97.4-99.9] % sensitivity for good recovery, which was superior to NSE < 33 μg/L (84.9 [79.3-89.4] % when used alone; 84.4 [78.8-89] % when combined with EEG, both p < 0.001). Highly malignant EEG had equal specificity (99.5 [97.4-99.9] %) but higher sensitivity than NSE for poor recovery. Further analysis of the discriminative power of outcome predictors revealed limited value of NSE over EEG. CONCLUSIONS: In the majority of comatose CA patients, the outcome remains indeterminate after application of ERC/ESICM prognostication algorithm. Standardized EEG background analysis enables accurate prediction of both good and poor recovery, thereby greatly reducing uncertainty about coma prognostication in this patient population.
PURPOSE: Post-resuscitation guidelines recommend a multimodal algorithm for outcome prediction after cardiac arrest (CA). We aimed at evaluating the prevalence of indeterminate prognosis after application of this algorithm and providing a strategy for improving prognostication in this population. METHODS: We examined a prospective cohort of comatose CApatients (n = 485) in whom the ERC/ESICM algorithm was applied. In patients with an indeterminate outcome, prognostication was investigated using standardized EEG classification (benign, malignant, highly malignant) and serum neuron-specific enolase (NSE). Neurological recovery at 3 months was dichotomized as good (Cerebral Performance Categories [CPC] 1-2) vs. poor (CPC 3-5). RESULTS: Using the ERC/ESICM algorithm, 155 (32%) patients were prognosticated with poor outcome; all died at 3 months. Among the remaining 330 (68%) patients with an indeterminate outcome, the majority (212/330; 64%) showed good recovery. In this patient subgroup, absence of a highly malignant EEG by day 3 had 99.5 [97.4-99.9] % sensitivity for good recovery, which was superior to NSE < 33 μg/L (84.9 [79.3-89.4] % when used alone; 84.4 [78.8-89] % when combined with EEG, both p < 0.001). Highly malignant EEG had equal specificity (99.5 [97.4-99.9] %) but higher sensitivity than NSE for poor recovery. Further analysis of the discriminative power of outcome predictors revealed limited value of NSE over EEG. CONCLUSIONS: In the majority of comatose CApatients, the outcome remains indeterminate after application of ERC/ESICM prognostication algorithm. Standardized EEG background analysis enables accurate prediction of both good and poor recovery, thereby greatly reducing uncertainty about coma prognostication in this patient population.
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