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Record W2080490340 · doi:10.1213/ane.0b013e318163f6a9

Does Tight Heart Rate Control Improve Beta-Blocker Efficacy? An Updated Analysis of the Noncardiac Surgical Randomized Trials

2008· review· en· W2080490340 on OpenAlexaff
W. Scott Beattie, Duminda N. Wijeysundera, Keyvan Karkouti, Stuart A. McCluskey, Gordon Tait

Bibliographic record

VenueAnesthesia & Analgesia · 2008
Typereview
Languageen
FieldMedicine
TopicHeart rate and cardiovascular health
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMedicineRandomized controlled trialBeta blockerInternal medicineCardiologyHeart failure

Abstract

fetched live from OpenAlex

BACKGROUND: Recent meta-analyses assessing the efficacy of perioperative beta-blockade trials have failed to show a reduction in postoperative morbidity and mortality. Tight control of heart rate (HR) has been suggested to improve these outcomes. Meta-analyses have not considered the influence of tight HR control on the efficacy of perioperative beta-blockade. METHODS: Using previously published search strategies, we identified all randomized trials evaluating perioperative beta-blockers after noncardiac surgery. This search yielded 10 trials with 2176 patients. We used the data from these studies to correlate measures of HR control with major postoperative outcomes, primarily in-hospital myocardial infarction (MI). Odds ratio (OR) and 95% confidence intervals (CI) were calculated, and metaregression was performed correlating measures of HR control with MI. RESULTS: The combined results of all studies did not show a significant cardioprotective effect of beta-blockers, with considerable heterogeneity among the studies (OR = 0.76; 95% CI = 0.4-1.4; P = 0.38 heterogeneity: I(2) = 34%). However, grouping the trials on the basis of maximal HR showed that trials where the estimated maximal HR was <100 bpm were associated with cardioprotection (OR = 0.23; 95% CI = 0.08-0.65; P = 0.005) whereas trials where the estimated maximal HR was >100 bpm did not demonstrate cardioprotection (OR = 1.17; 95% CI = 0.79-1.80; P = 0.43) with no heterogeneity. Moreover, metaregression of the HR response to beta-blockade against the log OR of postoperative MI demonstrated a linear association between the effect of beta-blockade on the mean, maximal, and variation in HR and the OR of an MI (r(2) = 0.63; P < 0.001) where a larger effect of beta-blockers on HR was associated with a decreased incidence of postoperative MI. Across all studies, beta-blockade resulted in a reduction in postoperative HR (weighted mean difference: 8.6 bpm; 95% CI = -9.6 to -7.6; I(2) = 85.3%) with considerable heterogeneity. This large heterogeneity in HR response to beta-blockade was found to be related, in part, to the type of beta-blocker, specifically, metoprolol, and the concomitant use of calcium channel blockers. Calcium channel blocker use and beta-blockers other than metoprolol resulted in more effective control of HR. There was wide variability in the HR response to beta-blockade. Twenty-five percent of patients receiving beta-blockers had episodes when the HRs were more than 100 bpm, although 15% of placebo patients also had bradycardia, which would have required a dose reduction had they been administered beta-blockers. Finally, this analysis found that perioperative beta-blockade was associated with an increased incidence of bradycardia (OR = 3.49; 95% CI = 2.4-5.9) and congestive heart failure (OR = 1.68; 95% CI = 1.00-2.8). CONCLUSIONS: The trials that achieve the most effective control of HR are associated with a reduced incidence of postoperative MI, suggesting that effective control of HR is important for achieving cardioprotection. Second, this analysis demonstrates that administration of beta-blockers does not reliably decrease HRs in all patients, and may be associated with increased side effects. Judicious use of combination therapy with other drugs may be necessary to achieve effective postoperative control of HR.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMeta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0260.023
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.343
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations113
Published2008
Admission routes1
Has abstractyes

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