RESUMEN
BACKGROUND: Postoperative atrial fibrillation (POAF) is the most common complication after cardiac surgery; it is associated with morbidity and mortality. We undertook this review to compare the effects of rhythm vs. rate control in this population. METHODS: We searched MEDLINE, Embase and CENTRAL to March 2023. We included randomized trials and observational studies comparing rhythm to rate control in cardiac surgery patients with POAF. We used a random-effects model to meta-analyze data and rated the quality of evidence using GRADE. RESULTS: From 8,110 citations, we identified 8 randomized trials (990 patients). Drug regimens used for rhythm control included amiodarone in four trials, other class III anti-arrhythmics in one trial, class I anti-arrhythmics in four trials and either a class I or III anti-arrhythmic in one trial. Rhythm control compared to rate control did not result in a significant difference in length of stay (mean difference -0.8 days; 95% CI -3.0 to +1.4, I2 = 97%), AF recurrence within 1 week (130 events; risk ratio [RR] 1.1; 95%CI 0.6-1.9, I2 = 54%), AF recurrence up to 1 month (37 events; RR 0.9; 95%CI 0.5-1.8, I2 = 0%), AF recurrence up to 3 months (10 events; RR 1.0; 95%CI 0.3-3.4, I2 = 0%) or mortality (25 events; RR 1.6; 95%CI 0.7-3.5, I2 = 0%). Effect measures from seven observational studies (1428 patients) did not differ appreciably from those in randomized trials. CONCLUSIONS: Although atrial fibrillation is common after cardiac surgery, limited low-quality data guide its management. Limited available evidence suggests no clear advantage to either rhythm or rate control. A large-scale randomized trial is needed to inform this important clinical question.
RESUMEN
BACKGROUND: Multicentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs. METHODS: Assuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure. RESULTS: While all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis. CONCLUSIONS: All six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.
