Propranolol As an Anxiolytic to Reduce the Use of Sedatives for Critically Ill Adults Receiving Mechanical Ventilation (PROACTIVE): An Open-Label Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: Surges in demand for sedatives for mechanical ventilation during the COVID-19 pandemic caused shortages of sedatives globally. Propranolol, a nonselective beta-adrenergic blocker, has been associated with reduced agitation and sedative needs in observational studies. We aimed to test whether propranolol could reduce the dose of sedatives needed in mechanically ventilated patients. DESIGN: Open-label randomized controlled trial. SETTING: Three academic hospitals. SUBJECTS: Any nonparalyzed patient receiving mechanical ventilation and requiring high-dose sedatives. INTERVENTIONS: Enteral propranolol 20-60 mg every 6 hours titrated to effect in the intervention group; all participants received protocol-titrated sedation with propofol or midazolam. MEASUREMENTS AND MAIN RESULTS: Mean change in 24 hours dose of sedative from baseline to day 3, proportion of sedation scores within target, and occurrence rate of adverse events. We enrolled a planned 72 patients between January 2021 and October 2022. Sixty-nine percent were male with a mean (sd) age of 54 years (15.91 yr). Most were admitted for COVID or non-COVID pneumonia. Intervention participants received propranolol for a mean of 10 days (mean daily dose, 90 mg). There was a significantly larger decrease in sedative dose from baseline (54% vs. 34%; p = 0.048) and more sedation assessments within target range (48% vs. 35%; p < 0.0001) in the intervention group compared with controls. There were no differences in mortality or adverse events. CONCLUSIONS: Propranolol is an inexpensive drug that effectively lowered the need for sedatives in critically ill patients managed in the COVID-19 pandemic. Propranolol may help preserve limited supplies of sedatives while achieving target sedation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.007 | 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".