Impact of Short-Acting Beta-Blockers on the Outcomes of Patients With Septic Shock: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To determine the impact of short-acting beta-blocker therapy on outcomes in adult patients with septic shock. DATA SOURCES: We searched MEDLINE, Embase, and unpublished sources from inception to April 19, 2024. STUDY SELECTION: We included randomized controlled trials (RCTs) that evaluated short-acting beta-blockers compared with usual care in patients with septic shock. DATA EXTRACTION: We collected data regarding study and patient characteristics, beta-blocker administration, and clinical, hemodynamic, and biomarker outcomes. DATA SYNTHESIS: Twelve RCTs proved eligible ( n = 1170 patients). Short-acting beta-blockers may reduce 28-day mortality (relative risk [RR], 0.76; 95% CI, 0.62-0.93; low certainty) and probably reduce new-onset tachyarrhythmias (RR, 0.37; 95% CI, 0.18-0.78; moderate certainty) but may increase the duration of vasopressors (mean difference [MD], 1.04 d; 95% CI, 0.37-1.72; low certainty). Furthermore, there is an uncertain effect as to whether short-acting beta blockers impact 90-day mortality (RR, 0.98; 95% CI, 0.73-1.31), ICU length of stay (MD, -0.75 d; 95% CI, -3.43 to 1.93 d), hospital length of stay (MD, 1.03 d; 95% CI, -1.92 to 3.98 d), duration of mechanical ventilation (MD, -0.10 d; 95% CI, -1.25 to 1.05 d) (all very low certainty), bradycardia episodes (RR, 3.14; 95% CI, 0.91-14.01), and hypotension episodes (RR, 4.74; 95% CI, 1.62-14.01) (all very low certainty). CONCLUSIONS: In patients with septic shock, short-acting beta-blockers may improve survival and reduce new-onset tachyarrhythmias. However, these findings were based on low certainty evidence and given ongoing concerns regarding adverse effects and the increase duration of vasopressor use, we need larger and more rigorous RCTs to evaluate this intervention.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".