Association between Prior Calcium Channel Blocker use and Mortality in Septic Patients: A Meta-Analysis of Cohort Studies
Bibliographic record
Abstract
Abstract Background: To comprehensively review the literature and synthesize evidence concerning the relationship between preadmission calcium channel blocker (CCB) use and mortality in patients with sepsis.Methods: The MEDLINE, EMBASE, and Cochrane CENTRAL databases were searched from their inception to April 9, 2020. Cohort studies that related to prior CCB use in patients with sepsis were analysed. Pairs of reviewers independently screened studies, extracted data, and assessed the risk of bias. Two primary outcomes related to mortality, namely, 30-day mortality and 90-day mortality, were analysed; heterogeneity between studies was assessed using I2 and was considered moderate if I2 was equivalent to 50–75% and high if I2 ≥ 75%. Fixed and random-effects models were used to calculate the pooled odds ratios (ORs) and 95% confidence intervals (CIs). The quality of outcomes was evaluated with the Newcastle-Ottawa Scale (NOS). Sensitivity analyses were performed to examine the robustness of the results.Results: 552 potentially relevant studies were identified, and the full texts of 25 articles were reviewed. Ultimately, five cohort studies involving 280,982 patients were confirmed to have a low risk of bias and were included. Preadmission CCB use was associated with a significantly lower 30-day mortality in septic shock (OR, 0.61 [0.38-0.97]; P = 0.035; I2 = 62.4%), not in sepsis (OR, 0.83 [0.66-1.04]; P = 0.103; I2 = 95.4%). Moreover, prior CCB use could significantly reduce 30-day mortality in sepsis (OR, 0.90 [0.85-0.95]; P < 0.001; I2 = 31.9%). Conclusions: This meta-analysis suggests that preadmission CCB use is significantly associated with improving long-term prognosis of sepsis, and also short-term survival of septic shock patients. This finding may provide an attractive direction for sepsis management.
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.045 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".