18 Continuous EEG for the Detection of Non-Convulsive Seizures in the PICU: A Quality Improvement Project
Bibliographic record
Abstract
Patients in the pediatric intensive care unit (PICU) are at risk for central nervous system insults, including seizures, potentially resulting in irreversible neurological damage if untreated. Seizures in critically ill patients are often clinically silent, impeding recognition and timely anticonvulsant treatment. Continuous electroencephalographic (cEEG) monitoring facilitates the detection of subclinical and non-convulsive seizures, allowing for early intervention and thereby improving neurological outcomes. Although cEEG monitoring is the current gold standard for detection of seizures in patients at risk for CNS injury, low availability of monitors, EEG technologists, and difficulty of bedside interpretation of recordings interfere with widespread use in PICUs. Recently, a consensus statement from the American Clinical Neurophysiology Society (ACNS) was published delineating indications for EEG monitoring in critically ill children. This quality improvement project included three aims. The first is to increase cEEG monitoring rates for PICU patients meeting ACNS indications for monitoring. The second aim is to characterize delays and barriers to cEEG recording. The third aim is to assess if cEEG monitoring informs medical management. We conducted a retrospective review of our children’s hospital electronic medical records, analyzing data of all patients admitted to the PICU from January 1st until June 23rd 2018 (retrospective period). This was followed by an 8-week mentorship period, consisting of educational interventions as well as joining the PICU daily patient rounds to help identify patients meeting cEEG monitoring criteria. Prevalence of patients meeting indications for monitoring, actual use of cEEG, barriers to timely cEEG application as well as cEEG influence on medical management were assessed. Prevalence of patients meeting cEEG monitoring indications were similar in both the retrospective and the mentorship period (18% vs. 23%). EEG monitoring rates of patients meeting cEEG indication criteria increased from 23% during the retrospective period to 100% at the end of the mentorship period. Of the seven cEEGs performed during the mentorship period, the median delay between meeting indications and initiation of monitoring was 17 hours, largely due to restrictions in the availability of technologists. All seven cEEGs informed anti-convulsive management. An educational intervention and mentorship period was highly effective in increasing cEEG monitoring usage in the PICU. However, limited hours of technologist availability for cEEG monitoring initiation represented the largest barrier to timely cEEG deployment. Future projects, including a pilot study of cEEG initiation by non-technologists, are currently being planned to facilitate timely access to cEEG monitoring.
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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 | 0.056 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".