Processed Electroencephalogram-Based Monitoring to Guide Sedation in Critically Ill Patients: A Systematic Review and International Expert Panel-Based Consensus Recommendations
Bibliographic record
Abstract
Abstract BackgroundThe literature related to the use of processed EEG (pEEG) for depth of sedation (DOS) monitoring is increasing, however it is unclear how to use this type of monitoring for critical care patients within the intensive care unit (ICU).MethodsWe performed a systematic review of the literature according to the Grade of Recommendation assessment, Development, and Evaluation (GRADE) approach. The modified Delphi method was utilised by a team of experts to produce statements and recommendations derived from study questions. Three separate online rounds discussing 89 statements categorized into four domains were formulated. The panelists rated the appropriateness of each statement and were able to suggest modifications or addition of statements. An analysis of anonymised ratings of the statements by part of the panel followed each Delphi round and previously validated criteria were used to define appropriateness and consensus.ResultsLevel of evidence regarding the four domains was very low. Fourteen panelists participated in the Delphi rounds and consensus was reached for 28 out of 89 statements, from which the reccomendations were created. The main findings were that DOS monitoring should be performed in critically ill patients whenever clinical evaluation is not possible, it should be performed by continuous pEEG techniques and the resulting data depicted with graphical tools to facilitate detection of excessive sedation, a potential cause of burst-suppression, and finally, structured training is suggested to achieve a basic pEEG competency.ConclusionsAlthough evidence on using DOS monitors in ICU is scarce and further research is required in order to better define the benefits of using pEEG, the results of this consensus highlight the general agreement that critically-ill patients would benefit from this type of neuromonitoring.
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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 | 0.128 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.020 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".