Evidence map›Paper›PMID 32016534›Full record

ArticleIntensive care medicine2020

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

Filippo Bongiovanni, Federico Romagnosi, Giuseppina Barbella, Arianna Di Rocco, Andrea O Rossetti, Fabio Silvio Taccone, Claudio Sandroni, Mauro Oddo

Abstract read
PubMed Publisher
In one paragraph

Article in Intensive care medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Guideline
  2. Guideline
  3. Pooled it
  4. Pooled it
  5. Guideline
  6. Trial
  7. Article
  8. Role of Neurophysiologic Testing in Prognostication of Hypoxic-Ischemic Encephalopathy.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2026
    Review
  9. Review
  10. Observational
  11. Article
  12. Article
  13. Article
  14. Article
  15. Observational
  16. Article
  17. Review
  18. Article
  19. Observational
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Filippo BongiovanniDepartment of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.
Federico RomagnosiDepartment of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.
Giuseppina BarbellaDepartment of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.
Arianna Di RoccoDepartment of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy.
Andrea O RossettiDepartment of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.
Fabio Silvio TacconeDepartment of Intensive Care Medicine, Erasme University Hospital, Brussels, Belgium.
Claudio SandroniDepartment of Anesthesiology and Intensive Care Medicine, Catholic University of The Sacred Heart, Rome, Italy.
Mauro OddoDepartment of Intensive Care Medicine, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland. Mauro.oddo@chuv.ch.ORCID 0000-0002-6155-2525

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePost-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.

methodsWe 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).

resultsUsing 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.

conclusionsIn 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.

Indexed as

Heart ArrestHypothermia, InducedComaElectroencephalographyHumansPhosphopyruvate HydratasePredictive Value of TestsPrognosisProspective StudiesUncertaintyPhosphopyruvate HydrataseCardiac arrestEEGGuidelinesNeuron-specific enolaseOutcomePrognostication

Identifiers

PMID32016534

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.