Meta-analysis and Evidence-based Medicine Training in Cardiology
The website is dedicated to meta-analysis and evidence-based medicine training in cardiology. It is sponsored by the Meta-analysis and Evidence-based medicine Training in Cardiology (METCARDIO) Group, currently headquartered in Turin, Italy, and formerly known as the Center for Overview, Meta-analysis, and Evidence-based Medicine Training (COMET). The focus of the website is on clinical research methods and evidence-based cardiovascular medicine with a specific interest in interventional cardiology and peripheral cardiovascular interventions. Nonetheless, there is plenty of training and research opportunities for other evidence-based endeavors, eg in anesthesiology, critical care medicine, and psychiatry.

Title: A network meta-analysis on thrombectomy devices
Highlights: Randomized trials focusing on one or more coronary thrombectomy devices will be systematically pooled and abstracted, obtaining individual, pooled pairwise, and pooled network risk estimates
Search strategy: PubMed will be searched for pertinent articles according to the following highly sensitive strategy: (thrombectomy OR (thrombus AND aspiration)) AND (coronary OR (myocardial AND infarction)) AND (randomized controlled trial[pt] OR controlled clinical trial[pt] OR randomized controlled trials[mh] OR random allocation[mh] OR double-blind method[mh] OR single-blind method[mh] OR clinical trial[pt] OR clinical trials[mh] OR (clinical trial[tw] OR ((singl*[tw] OR doubl*[tw] OR trebl*[tw] OR tripl*[tw]) AND (mask*[tw] OR blind[tw])) OR (latin square[tw]) OR placebos[mh] OR placebo*[tw] OR random*[tw] OR research design[mh:noexp] OR follow-up studies[mh] OR prospective studies[mh] OR cross-over studies[mh] OR control*[tw] OR prospectiv*[tw] OR volunteer*[tw]) NOT (animal[mh] NOT human[mh]) NOT (comment[pt] OR editorial[pt] OR meta-analysis[pt] OR practice-guideline[pt] OR review[pt]))
Principal investigator: Giuseppe Biondi-Zoccai, Sapienza University of Rome, Latina, Italy

Title: Current and New Oral Antithrombotics in Nonvalvular Atrial Fibrillation: A Network Meta-analysis of 79,808 patients
First author: Ariel Dogliotti, MD, Unidad de Epidemiología Clínica y Estadística, Grupo Oroño, Bvrd Oroño 450, 2000 Rosario (Santa Fe), Argentina. Phone: +54 3414203040. Fax: +54 3414203040. Email:
Corresponding author: Robert P. Giugliano, MD, SM, Brigham and Women´s Hospital, Harvard Medical School, TIMI Study Group, 350 Longwood Avenue, 1st Floor Offices, Boston, MA 02115 USA. Phone: +1 6172780145. Fax: +1 6177347320. Email:
Search strategy: We searched Medline, Embase, and the Cochrane database of systematic reviews through May 2013 with no language restriction using the following Medical Subject Heading and keywords: anticoagulant, antiplatelet, aspirin, clopidogrel, warfarin, vitamin K antagonists, dabigatran, apixaban, rivaroxaban, atrial fibrillation, atrial arrhythmias, coupled with outcome searched using the terms stroke, cerebrovascular accident, transient ischemic attack, and TIA. We also reviewed the reference lists of published meta-analyses of anticoagulant and antiplatelet therapies to prevent stroke and embolic events in patients with atrial fibrillation. Overall, 20 manuscripts were included in our analyses.
Additional details: Inclusion criteria for retrieved studies were: a) randomized controlled phase II or III trials of VKAs, ASA, clopidogrel, and novel oral anticoagulants in patients with nonvalvular atrial fibrillation; b) randomized treatment allocation, c) intention-to-treat analysis and d) follow up more than one year. In order to reflect current practice patterns, we excluded studies or study arms where VKAs were administered at non-standard doses (e.g., low fixed doses) or antiplatelet agents other than ASA or clopidogrel were tested.
Two independent reviewers performed data abstraction. We used consensus to resolve discrepancies. The endpoints of interest were: stroke, composite of ischemic stroke or systemic embolism, death from any cause, and major bleeding. Definitions of endpoints were the same across all trials, with the exception of major bleeding where we used the trial-specific definition. We used data from the intention-to-treat populations, unless otherwise specified.
First, pair-wise meta-analyses with a random-effects model were carried out. We used visual inspection of the forest plots and the I2 statistic to investigate the possibility of statistical heterogeneity (Higgins-Thompson method: low heterogeneity 25% moderate heterogeneity 50% and high heterogeneity 75%. All data were analyzed using a Stata version 9. Second, a random-effects model multi treatment meta-analysis within a Bayesian framework using Markov Chain Monte Carlo simulation) was performed. Multiple treatment meta-analysis was performed using GeMTC R package. The statistical analysis is based on binomial likelihoods with a logit link function. GeMTC automatically specified vague prior distributions for the trial baseline effects, the relative effects (normal with mean 0 and standard deviation 37.5), and the random effects standard deviation (uniform in the interval 0 to 2.5). We used a technique known as "node-splitting" to evaluate for inconsistency in the findings of the network meta-analysis coming from direct vs. indirect evidence. Node-splitting assesses whether direct and indirect evidence on a specific node (the split node) are in agreement. The basic idea is that, for the node of interest, two posterior distributions are generated from independent sources: trials that directly compare two interventions and trials that do not. Measures of con?ict between these sources are identified from the measures of compatibility between these two posterior distributions. Direct and indirect estimates of effect and the corresponding Bayesian "p-values" for inconsistency were calculated. We expressed the comparative effectiveness of the treatments as the odds ratio (OR) of an outcome, with 95% credibility intervals (CrIs). The credibility interval is the Bayesian analogue to confidence intervals used in traditional frequentist statistical approaches. A result was considered "significant" if the CrI did not include 1.0. We also ranked the different interventions in terms of their likelihood of leading to the best results for each outcome. In each Markov chain Monte Carlo cycle, each treatment j is ranked according to the estimated effect size. Then, the proportion of the cycles in which a given treatment ranks first out of the total gives the probability P(j =1) that treatment j ranks first, that is, ''is the best'' among the a available treatment options. Similar probabilities are calculated for being the second best, the third best, and so on -P(j = b), b = 1,…, a. These probabilities sum to one for each treatment and each rank. Considering different lengths of follow-up for individual trials, and to account for censored data, we obtained the rates of outcomes for all trials and calculated the log hazard ratios (assuming a constant rate of hazards for individual trials) from the event rate reported and mean duration of follow-up. Sensitivity Analysis We performed a number of sensitivity analyses including and excluding specific studies that utilized substantial different methods to report or analyze their endpoint data or had important differences in study entry criteria. Since major bleeding data in the RE-LY trial was presented and analyzed as "on treatment", we summarized the data from the studies as published, and then excluded the data from RE-LY in a sensitivity analysis. Because the SPAF-I, EAFT, PATAF, ACTIVE (A) and AVERROES studies each included treatment arms of patients whom VKAs therapy was unsuitable, we performed sensitivity analyses excluding these treatments. Sensitivity analyses that eliminated 2 phase II trials and 2 smaller phase III also were performed. Finally, since the ROCKET-AF trial by design included only patients at high risk of stroke, we also performed a sensitivity analysis excluding the data from this one study. As an additional sensitivity analysis, we did a network meta-analysis omputing hazard ratios with a Poisson regression model and random effects method, as such analyses explicitly exploit differences in follow-up between studies, thus maximizing precision and validity.

Title: Network meta-analysis of permanent-polymer drug-eluting stents versus bioabsorbable-polymer biolimus eluting stents
Highligths: Randomized trials comparing bare-metal stents, permanent-polymer drug-eluting stents (everolimus-, paclitaxel-, sirolimus-, and zotarolimus-eluting stents), and bioabsorbable-polymer biolimus-eluting stents for percutaneous coronary intervention will be systematically searched and pooled with pairwise and network meta-analytic methods using WinBUGS computing odds ratios and hazard ratios. The outcomes of interest will be death, myocardial infarction, target vessel revascularization, and stent thrombosis.
Principal Investigator: Tullio Palmerini, MD, Sant'Orsola Hospital, Bologna, Italy