How and Whom to Monitor for Seizures in an ICU: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To pool prevalence of nonconvulsive seizure, nonconvulsive status epilepticus, and epileptiform activity detected by different electroencephalography types in critically ills and to compare detection rates among them. DATA SOURCES: MEDLINE (via PubMed) and SCOPUS (via Scopus) STUDY SELECTION:: Any type of study was eligible if studies were done in adult critically ill, applied any type of electroencephalography, and reported seizure rates. Case reports and case series were excluded. DATA EXTRACTION: Data were extracted independently by two investigators. Separated pooling of prevalence of nonconvulsive seizure/nonconvulsive status epilepticus/epileptiform activity and odds ratio of detecting outcomes among different types of electroencephalography was performed using random-effect models. This meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and also adhered to the Meta-analyses Of Observational Studies in Epidemiology guidelines. Quality of evidence was assessed with the Newcastle-Ottawa Quality Assessment Scale for observational studies and Cochrane methods for randomized controlled trial studies. DATA SYNTHESIS: A total of 78 (16,707 patients) and eight studies (4,894 patients) were eligible for pooling prevalence and odds ratios. For patients with mixed cause of admission, the pooled prevalence of nonconvulsive seizure, nonconvulsive status epilepticus, either nonconvulsive seizure or nonconvulsive status epilepticus detected by routine electroencephalography was 3.1%, 6.2%, and 6.3%, respectively. The corresponding prevalence detected by continuous electroencephalography monitoring was 17.9%, 9.1%, and 15.6%, respectively. In addition, the corresponding prevalence was high in post convulsive status epilepticus (33.5%, 20.2%, and 32.9%), CNS infection (23.9%, 18.1%, and 23.9%), and post cardiac arrest (20.0%, 17.3%, and 22.6%). The pooled conditional log odds ratios of nonconvulsive seizure/nonconvulsive status epilepticus detected by continuous electroencephalography versus routine electroencephalography from studies with paired data 2.57 (95% CI, 1.11-5.96) and pooled odds ratios from studies with independent data was 1.57 (95% CI, 1.00-2.47). CONCLUSIONS: Prevalence of seizures detected by continuous electroencephalography was significantly higher than with routine electroencephalography. Prevalence was particularly high in post convulsive status epilepticus, CNS infection, and post cardiac arrest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.059 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".