548 Volatile Sedation in the Burn Intensive Care Unit: A Quality Improvement Initiative
Bibliographic record
Abstract
Abstract Introduction Achieving optimal sedation and analgesia in patients with major burn injuries can be challenging. Reliance on benzodiazepines results in longer durations of mechanical ventilation, delirium and withdrawal. Recent evidence and local experience in our critical care unit has supported the application of volatile agents such as isoflurane for sedation. A local audit determined that 70% of mechanically ventilated patients in our burn center are managed on 3 or more continuous sedative infusions. The aim of this initiative was to reduce the number of continuous sedative infusions to maintain target sedation range (SAS 2-4) by 25% in the 48 hour period following initiation of isoflurane. Methods A multifaceted education approach was applied to engage all bedside staff, including burn surgeons, respiratory therapists and nurses. Training occurred though self-directed modules, in-person learning and just-in-time training upon new patient initiation. Data from the 18 month period since implementation were collected retrospectively via manual chart review. Results Since implementation in March 2021, 18 patients were sedated using isoflurane in our burn intensive care unit (ICU). Within 48 hours of isoflurane initiation, 50% (9/18) of patients had a decrease in the number of sedative infusions and 56% (10/18) obtained an optimal level of sedation using isoflurane and hydromorphone alone. Conclusions Volatile agents provide a safe and reliable method of sedation in mechanically ventilated patients with major burn injuries. Despite limitations related to specialized equipment and training, the introduction of this strategy has been well received, with positive feedback relating to both ease of implementation and titration. Applicability of Research to Practice Patients with major burn injuries require multiple painful surgeries and dressing changes. Volatile sedation has been shown to reduce the number of sedative infusions, specifically, our reliance on benzodiazepines.
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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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".