Evidence map›Paper›PMID 42144833›Full record

ArticleAnnals of neurology2026

Continuous Versus Short EEG After Ischemic Stroke: What cEEG Adds for Detecting Abnormalities and Predicting Post-Stroke Epilepsy.

Kai Michael Schubert, Vijaya Dasari, Chiara Tatillo, Gilles Naeije, Sándor Beniczky, Carla Bentes, Marian Galovic, Nicolas Gaspard, Vineet Punia

Abstract readComparative Study
In one paragraph

Article in Annals of neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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.

Kai Michael SchubertDepartment of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0003-1438-7544
Vijaya DasariEpilepsy Center, Cleveland Clinic, Cleveland, OH.
Chiara TatilloDepartment of Neurology, Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium.
Gilles NaeijeDepartment of Neurology, Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium.
Sándor BeniczkyDepartment of Clinical Neurophysiology, Aarhus University Hospital, Aarhus and Danish Epilepsy Centre, Dianalund, Denmark.ORCID https://orcid.org/0000-0002-6035-6581
Carla BentesDepartment of Neurosciences and Mental Health (Neurology), Hospital de Santa Maria - ULSSM, Centro de Estudos Egas Moniz, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.
Marian Galovic *Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland.ORCID https://orcid.org/0000-0002-2307-071X
Nicolas Gaspard *Department of Neurology, Hôpital Universitaire de Bruxelles - Hôpital Erasme, Brussels, Belgium.
Vineet Punia *Epilepsy Center, Cleveland Clinic, Cleveland, OH.ORCID https://orcid.org/0000-0002-0552-6736

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe objective of this study was to quantify incremental diagnostic yield and prognostic value of continuous electroencephalography (cEEG; ≥12 hours) versus a 60-minute short electroencephalography (sEEG) in predicting post-stroke epilepsy (PSE) in patients without acute symptomatic seizures.

methodsWe retrospectively included 283 adults who underwent cEEG within 7 days; sEEG comprised the first 60 minutes of the same recording. EEGs were interpreted using American Clinical Neurophysiology Society (ACNS) terminology by neurophysiologists blinded to outcomes. Within-patient yield was quantified using odds ratios (ORs) with 95% confidence intervals (CIs). PSE were modeled using Fine-Gray competing-risks regression (death as competing event) and reported as subdistribution hazard ratios (sHR). SeLECT-EEG derived from sEEG and cEEG was compared using C-index and net reclassification improvement (NRI).

resultsOver a median follow-up of 41 months (interquartile range [IQR] = 22-64), 41 of 283 patients (14.5%) developed PSE. Compared to sEEG, cEEG increased detection of interictal epileptiform discharges (11 vs 3%, OR = 3.75, 95% CI = 1.75-8.02, p < 0.001) and electrographic seizures (4 vs 0.7%, OR = 6.22, 95% CI = 1.38-28.06, p = 0.01). Lateralized periodic discharges (sHR = 4.50, 95% CI = 2.13-9.51) and electrographic seizures (sHR = 3.63, 95% CI = 1.52-8.63) were the strongest predictors of PSE. The cEEG-derived SeLECT-EEG improved discrimination versus sEEG-derived scoring (ΔC-index 0.055, 95% CI = 0.012-0.101, p = 0.014) and reclassification (NRI = 0.25, 95% CI = 0.07-0.42). Epileptiform activity emerging after the first hour conferred higher 5-year PSE risk than never detected (28 vs 11%, Gray p = 0.006).

interpretationThe cEEG identifies additional epileptiform abnormalities with prognostic value beyond routine-duration EEG, supporting extension of monitoring in selected cases based on baseline risk and early EEG findings. ANN NEUROL 2026;100:400-415.

Indexed as

ElectroencephalographyEpilepsyIschemic StrokeStrokeAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedPredictive Value of TestsPrognosisRetrospective Studies

Identifiers

PMID42144833
PMCPMC13387982

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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.