Bispectral Index Can Reliably Detect Deep Sedation in Mechanically Ventilated Patients: A Prospective Multicenter Validation Study
Bibliographic record
Abstract
BACKGROUND: Excessively deep sedation is prevalent in mechanically ventilated patients and often considered suboptimal. We hypothesized that the bispectral index (BIS), a quantified electroencephalogram instrument, would accurately detect deep levels of sedation. METHODS: We prospectively enrolled 90 critically ill mechanically ventilated patients who were receiving sedation. The BIS was monitored for 24 hours and compared with the Richmond Agitation Sedation Scale (RASS) evaluated every 4 hours. Deep sedation was defined as a RASS of -3 to -5. Threshold values of baseline BIS (the lowest value before RASS assessment) and stimulated BIS (the highest value after standardized assessment) for detecting deep sedation were determined in a training set (45 patients, 262 RASS assessments). Diagnostic accuracy was then analyzed in a validation set (45 patients, 264 RASS assessments). RESULTS: Deep sedation was only prescribed in 6 (6.7%) patients, but 76 patients (84.4%) had at least 1 episode of deep sedation. Thresholds for detecting deep sedation of 50 for baseline and 80 for stimulated BIS were identified, with respective areas under the receiver-operating characteristic curve of 0.771 (95% confidence interval, 0.714-0.828) and 0.805 (0.752-0.857). The sensitivity and specificity of baseline BIS were 94.0% and 66.5% and of stimulated BIS were 91.0% and 66.5%. When baseline and stimulated BIS were combined, the sensitivity, specificity, and clinical utility index were 85.0% (76.1%-91.1%), 85.9% (79.5%-90.7%), and 66.9% (57.8%-76.0%), respectively. CONCLUSIONS: Combining baseline and stimulated BIS may help detect deep sedation in mechanically ventilated patients.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".