Evidence map›Paper›PMID 33287874›Full record

ArticleCritical care (London, England)2020

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies.

Michael Müller, Andrea O Rossetti, Rebekka Zimmermann, Vincent Alvarez, Stephan Rüegg, Matthias Haenggi, Werner J Z'Graggen, Kaspar Schindler, Frédéric Zubler

Open access · goldAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Critical care (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.8field-weighted citation impact, top 8% of its field
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

10 citing papers in PubMed, 20 citations in OpenAlex.

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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

9 authors at 4 institutions in 1 country.

Michael MüllerDepartment of Neurology, Sleep-Wake-Epilepsy-Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Andrea O RossettiDepartment of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
Rebekka ZimmermannDepartment of Neurology, Sleep-Wake-Epilepsy-Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Vincent AlvarezDepartment of Neurology, Hôpital du Valais, Sion, Switzerland.
Stephan RüeggDepartment of Neurology, University Hospital Basel, Basel, Switzerland.
Matthias HaenggiDepartment of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Werner J Z'GraggenDepartment of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Kaspar SchindlerDepartment of Neurology, Sleep-Wake-Epilepsy-Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Frédéric ZublerDepartment of Neurology, Sleep-Wake-Epilepsy-Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland. frederic.zubler@gmail.com.ORCID 0000-0003-0090-7297
University of Bern · CHHôpital du Valais · CHUniversity Hospital of Basel · CHUniversity of Lausanne · CH

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 320030_169379
6 · The paper itself

Abstract

backgroundEarly prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI).

methodData from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2).

resultsThe area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance.

conclusionsWhile prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.

Indexed as

Predictive Value of TestsAdultArea Under CurveConsciousness DisordersElectroencephalographyFemaleHumansMaleMiddle AgedOutcome Assessment, Health CarePrognosisProspective StudiesRetrospective StudiesROC CurveSwitzerlandAcute consciousness impairmentElectroencephalographyHypoxic ischemic encephalopathyPrognosticationRandom forestTraumatic brain injury

Identifiers

PMID33287874
PMCPMC7720582
OpenAlexW3112089594

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Registered trials

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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.