ArticleCritical care medicine2026
Point-of-Care EEG Artificial Intelligence Measure of Seizure Burden Associates With Clinical Outcome at Discharge.
Article in Critical care medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesPoint-of-care (POC) electroencephalography (EEG) enabled with artificial intelligence (AI) algorithms hold the potential to address gaps in EEG access and interpretation. We assessed the clinical association of the patterns recognized by one of such systems.
designSecondary cohort analysis of the retrospective multicenter Seizure Assessment and Forecasting with Efficient Rapid-EEG (SAFER-EEG) study.
settingEEG and clinical data were gathered from three academic centers with access to POC EEG (Ceribell, Sunnyvale, CA) as part of their standard of care. We used a bedside seizure burden (SzB) monitor ("bedside" algorithm) and a more sensitive online algorithm ("portal" algorithm) to determine SzB. The modified Rankin Scale (mRS) score at discharge was selected as the outcome. PATIENTS: Four hundred enrolled adult patients, 359 with complete outcome and clinical data.
interventionsTime in seizure and peak SzB assessed by AI. MEASUREMENTS AND MAIN
resultsPer the bedside algorithm, 39.8% of patients had peak 5-minute SzB greater than 0%. With every additional hour of seizure detected by the bedside algorithm, patients were more likely to have unfavorable outcome (mRS > 3) at discharge (adjusted odds ratio [aOR], 1.98; 95% CI, 1.11-4.29). Compared with 5-minute SzB = 0%, prolonged activity per the bedside algorithm (peak 5-min SzB ≥ 90%) was associated with a 3.4-fold increase in aOR of poor outcome. Every 30 seconds of activity in the maximum hourly SzB of the bedside algorithm was associated with increased odds of unfavorable outcome (aOR, 1.02; 95% CI, 1.00-1.03). Combining outputs from the algorithms increased the strength of associations with outcome, particularly in patients with peak 5-minute SzB greater than or equal to 90% (aOR, 4.4; 95% CI, 1.66-12.69).
conclusionsAI-estimated SzB is associated with functional outcomes at discharge in a dose-response fashion, even after controlling for clinical cofounds. This provides initial evidence of the clinical utility of AI algorithms for detecting clinically important EEG patterns.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.