The Sedative Effect of Propranolol on Critically Ill Patients: A Case Series
Bibliographic record
Abstract
Introduction Recent studies have examined the effectiveness of alpha-2-adrenergic agonists for controlling delirium and agitation. Propranolol, a non-selective beta-adrenergic antagonist with good penetration of the blood brain barrier, has not been investigated for this purpose. Material and Methods We retrospectively reviewed the medical records of all patients who were prescribed propranolol in our Medical-Surgical ICU from January 1, 2010 to December 31, 2013. We recorded the sedation level and daily dose of sedatives, analgesics, and antipsychotics administered each day for 6 days after starting propranolol, and compared them to the day before starting propranolol. Results Sixty-four patients met inclusion criteria. Thirty-eight episodes met exclusion criteria, leaving 27 patients (31 episodes). The administration of propranolol was associated with significant reductions in fentanyl equivalents (65%, P=0.009), midazolam equivalents (57%, p=0.048), propofol (16%, p=0.009), and haloperidol (44%, p=0.024) on Day 2 after starting propranolol compared with baseline. A stratified analysis showed that these decreases were seen regardless of clinical improvement or deterioration. Conclusion The use of propranolol was associated with a significant reduction in doses of sedatives and analgesia. Further studies are needed to determine whether propranolol may be a useful adjuvant for managing delirium and agitation in the ICU.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".