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Standardized EEG analysis to reduce the uncertainty of outcome prognostication after cardiac arrest

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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.

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Abbreviations

BS:

Burst suppression;

BSR:

Brainstem reflexes (including pupillary and corneal)

CA:

Cardiac arrest

CPC:

Cerebral Performance Category

CT:

Brain computerized tomography

ERC:

European Resuscitation Council

ESICM:

European Society of Intensive Care Medicine

EEG:

Electroencephalogram

FPR:

False positive rate

GCS:

Glasgow Coma Scale

ICU:

Intensive care unit

MRI:

Brain magnetic resonance imaging

NPV:

Negative predictive value

NSE:

Serum neuron-specific enolase

PPV:

Positive predictive value

ROC:

Receiving operator characteristic

SE:

Status epilepticus

SSEP:

Somatosensory-evoked potentials

WLST:

Withdrawal of life-sustaining therapies

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Acknowledgements

The authors thank John-Paul Miroz, RN, Jan Novy, MD PhD, Christine Stähli, RN and Laura Pezzi, RN, for their help in data collection, and EEG retrieval and analysis.

Funding

Mauro Oddo and Andrea O. Rossetti are supported by the Swiss National Science Foundation.

Author information

Author notes

  1. Claudio Sandroni and Mauro Oddo contributed equally.

Authors and Affiliations

  1. Department of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland

    Filippo Bongiovanni, Federico Romagnosi & Mauro Oddo

  2. Department of Anesthesiology and Intensive Care Medicine, Catholic University of The Sacred Heart, Rome, Italy

    Filippo Bongiovanni & Claudio Sandroni

  3. Section of Anaesthesiology and Intensive Care, Department of Surgery, Dentistry, Paediatrics and Gynaecology, University Hospital Integrated Trust of Verona, Verona, Italy

    Federico Romagnosi

  4. Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland

    Giuseppina Barbella & Andrea O. Rossetti

  5. Neurology Unit, San Gerardo Hospital, School of Medicine and Surgery and Milan-Center for Neuroscience (NeuroMI), University of Milano-Bicocca, Milan, Italy

    Giuseppina Barbella

  6. Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy

    Arianna Di Rocco

  7. Department of Intensive Care Medicine, Erasme University Hospital, Brussels, Belgium

    Fabio Silvio Taccone

Authors
  1. Filippo Bongiovanni
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  2. Federico Romagnosi
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  3. Giuseppina Barbella
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  6. Fabio Silvio Taccone
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  7. Claudio Sandroni
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  8. Mauro Oddo
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Contributions

FB and FR equally contributed to data acquisition and analysis, and drafted the manuscript; GB performed EEG analysis and classification; ADR provided expert supervision of statistical analysis; AOR supervised EEG analysis and classification, contributed to data acquisition and analysis, and critically revised the manuscript; FST revised the manuscript and provided important intellectual contribution; CS and MO equally contributed to supervise the concept and design of the study, data analysis and interpretation, and critically revised the manuscript. All authors approved and agreed on the final version of the manuscript. This article is reported according to the STARD 2015 guidelines (https://www.equator-network.org/reporting-guidelines/stard/).

Corresponding author

Correspondence to Mauro Oddo.

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Conflicts of interest

The authors declare that they have no competing interests.

Ethics approval

Approval was obtained from the Ethical Research Committee of the University of Lausanne, and waiver of consent was allowed since all examinations were part of standard patient care.

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Bongiovanni, F., Romagnosi, F., Barbella, G. et al. Standardized EEG analysis to reduce the uncertainty of outcome prognostication after cardiac arrest. Intensive Care Med 46, 963–972 (2020). https://doi.org/10.1007/s00134-019-05921-6

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