Calcium Channel Blockers for Reducing Cardiac Morbidity After Noncardiac Surgery: A Meta-Analysis
Bibliographic record
Abstract
In Brief Cardiac complications are the leading cause of death after noncardiac surgery. Despite theoretical benefits, calcium channel blockers (CCB) are not widely used in the perioperative setting. This systematic review assessed the efficacy of CCBs during noncardiac surgery. MEDLINE, EMBASE, Science Citation Index, PubMed, and reference lists were searched without language restriction for randomized controlled trials (RCT) evaluating CCBs during noncardiac surgery. Two reviewers independently abstracted data on death, myocardial infarction (MI), ischemia, supraventricular tachyarrhythmia (SVT), and congestive heart failure (CHF). Treatment effects were calculated as relative risks (RR) with 95% confidence intervals (CI). Eleven studies (1007 patients) were included. CCBs significantly reduced ischemia (RR, 0.49; 95% CI, 0.30–0.80; P = 0.004) and SVT (RR, 0.52; 95% CI, 0.37–0.72; P < 0.0001). CCBs were associated with trends towards reduced death and MI. In post hoc analyses, CCBs significantly reduced death/MI (RR, 0.35; 95% CI, 0.15–0.86; P = 0.02) and major morbid events (MME), defined as death, MI, or CHF (RR, 0.39; 95% CI, 0.17–0.89; P = 0.02). In subgroup analyses, diltiazem significantly reduced ischemia, SVT, death/MI, and MMEs. This meta-analysis shows CCBs significantly reduced ischemia, SVT, and combined end-points in the setting of noncardiac surgery. The majority of these benefits are attributable to diltiazem, suggesting the need for further evaluation of this drug in a large RCT. IMPLICATIONS: This meta-analysis evaluated the efficacy of calcium channel-blockers (CCB) for preventing cardiac complications after noncardiac surgery. Eleven relevant randomized controlled trials were identified. Overall, CCBs reduced major cardiac morbid events, with most benefits being attributable to diltiazem.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".