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Cardiac rehabilitation is strongly recommended in patients after acute myocardial infarction.To assess cardiac rehabilitation prescription after acute myocardial infarction according to predicted risk, and its association with 1-year mortality, using the FAST-MI registries.We used data from three 1-month French nationwide registries, conducted 5 years apart from 2005 to 2015, including 13130 patients with acute myocardial infarction admitted to coronary or intensive care units. Atherothrombotic risk stratification was performed using the Thrombolysis In Myocardial Infarction Risk Score for Secondary Prevention (TRS-2P). Patients were classified into three categories: Group 1 (low risk; no or one risk indicator; score of 0 or 1); Group 2 (intermediate risk; two risk indicators; score of 2); and Group 3 (high risk; at least three risk indicators; score of≥3).Among the 12291 patients, cardiac rehabilitation prescription was 43.6% (49.9% in Group 1; 43.0% in Group 2; 35.2% in Group 3). Using Cox multivariable analysis, cardiac rehabilitation prescription was associated with lower mortality at 1 year in the overall population (3.8% vs. 8.2%; hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.61-0.85; P0.001). Cardiac rehabilitation was associated with improved 1-year mortality, with homogeneous relative risk reductions in low- and intermediate-risk categories (HR 0.70, 95% CI 0.51-0.94) compared with high-risk patients (HR 0.72, 95% CI 0.59-0.88). In absolute terms, however, mortality decrease associated with cardiac rehabilitation was positively correlated with risk level (Group 1, 0.9% vs. 2.4%; Group 2, 3.0% vs. 4.2%; Group 3, 10.5% vs. 17.3%).Cardiac rehabilitation prescription was inversely correlated with patient risk. A positive association between cardiac rehabilitation and 1-year survival after acute myocardial infarction was present whatever the risk level, but the greatest mortality reduction was observed in high-risk patients.

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AimsVancomycin is one of the most evaluated antibiotics in neonates using modeling and simulation approaches. However no clear consensus on optimal dosing has been achieved. The objective of the present study was to perform an external evaluation of published models, in order to test their predictive performances in an independent dataset and to identify the possible study-related factors influencing the transferability of pharmacokinetic models to different clinical settings.MethodPublished neonatal vancomycin pharmacokinetic models were screened from the literature. The predictive performance of six models was evaluated using an independent dataset (112 concentrations from 78 neonates). The evaluation procedures used simulation-based diagnostics [visual predictive check (VPC) and normalized prediction distribution errors (NPDE)].ResultsDifferences in predictive performances of models for vancomycin pharmacokinetics in neonates were found. The mean of NPDE for six evaluated models were 1.35, −0.22, −0.36, 0.24, 0.66 and 0.48, respectively. These differences were explained, at least partly, by taking into account the method used to measure serum creatinine concentrations. The adult conversion factor of 1.3 (enzymatic to Jaffé) was tested with an improvement in the VPC and NPDE, but it still needs to be evaluated and validated in neonates. Differences were also identified between analytical methods for vancomycin.ConclusionThe importance of analytical techniques for serum creatinine concentrations and vancomycin as predictors of vancomycin concentrations in neonates have been confirmed. Dosage individualization of vancomycin in neonates should consider not only patients' characteristics and clinical conditions, but also the methods used to measure serum creatinine and vancomycin.

Additional file 1. SPIRIT 2013 Checklist.

Additional file 1. SPIRIT 2013 Checklist.

Background Large sample sizes are often required to detect statistically significant associations between pharmacogenetic markers and treatment response. Meta-analysis may be performed to synthesize data from several studies, increasing sample size and, consequently, power to detect significant genetic effects. However, performing robust synthesis of data from pharmacogenetic studies is often challenging because of poor reporting of key data in study reports. There is currently no guideline for the reporting of pharmacogenetic studies that has been developed using a widely accepted robust methodology. The objective of this project was to develop the STrengthening the Reporting Of Pharmacogenetic Studies (STROPS) guideline. Methods and findings We established a preliminary checklist of reporting items to be considered for inclusion in the guideline. We invited representatives of key stakeholder groups to participate in a 2-round Delphi survey. A total of 52 individuals participated in both rounds of the survey, scoring items with regards to their importance for inclusion in the STROPS guideline. We then held a consensus meeting, at which 8 individuals considered the results of the Delphi survey and voted on whether each item ought to be included in the final guideline. The STROPS guideline consists of 54 items and is accompanied by an explanation and elaboration document. The guideline contains items that are particularly important in the field of pharmacogenetics, such as the drug regimen of interest and whether adherence to treatment was accounted for in the conducted analyses. The guideline also requires that outcomes be clearly defined and justified, because in pharmacogenetic studies, there may be a greater number of possible outcomes than in other types of study (for example, disease–gene association studies). A limitation of this project is that our consensus meeting involved a small number of individuals, the majority of whom are based in the United Kingdom. Conclusions Our aim is for the STROPS guideline to improve the transparency of reporting of pharmacogenetic studies and also to facilitate the conduct of high-quality systematic reviews and meta-analyses. We encourage authors to adhere to the STROPS guideline when publishing pharmacogenetic studies., Marty Chaplin and co-authors recount the development of a guideline for reporting pharmacogenetic studies.