New National Institute for Health and Care Excellence guidance for hypertension: a review and comparison with the US and European guidelines.

The UK National Institute for Health and Care Excellence (NICE) guidance for hypertension management has recently been updated. This review article summaries the main recommendations in NICE guidelines, and compares them with the American and European guidelines. NICE and the European Society of Cardiology (ESC) recommend diagnosing hypertension at a higher level than the American College of Cardiology/American Heart Association (ACC/AHA). NICE treats to less stringent targets than both the ACC/AHA and the ESC, while using similar, although non-combination pill based, treatment regimens.

Advances in Multi-Modality Imaging in Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is characterized by abnormal growth of the myocardium with myofilament disarray and myocardial hyper-contractility, leading to left ventricular hypertrophy and fibrosis. Where culprit genes are identified, they typically relate to cardiomyocyte sarcomere structure and function. Multi-modality imaging plays a crucial role in the diagnosis, monitoring, and risk stratification of HCM, as well as in screening those at risk. Following the recent publication of the first European Society of Cardiology (ESC) cardiomyopathy guidelines, we build on previous reviews and explore the roles of electrocardiography, echocardiography, cardiac magnetic resonance (CMR), cardiac computed tomography (CT), and nuclear imaging. We examine each modality's strengths along with their limitations in turn, and discuss how they can be used in isolation, or in combination, to facilitate a personalized approach to patient care, as well as providing key information and robust safety and efficacy evidence within new areas of research.

Intracluster correlation coefficients and reliability of randomized multicenter stroke trials within VISTA.

BACKGROUND: Reliable estimates of intracluster correlation coefficients (ICCs) for specific outcome measures are crucial for sample size calculations of future cluster randomized trials. ICCs indicate the proportion of data variability that is explained by defined levels of clustering. AIMS: In this manuscript, we present potentially valuable and reliable estimates of ICCs for specific baseline and follow-up data. METHOD: ICCs were estimated from linear and generalized linear mixed models using maximum likelihood estimation for common measures used in stroke research, including modified Rankin Scale (mRS), National Institutes of Health Stroke Scale (NIHSS), and Barthel Index (BI). RESULTS: Data were available for 11 841 patients with ischemic stroke from 11 randomized trials. After adjusting for age, thrombolysis, and baseline NIHSS, the median ICC for follow-up data, using center as the level of clustering, ranged from 0·007 to 0·041. The ICCs using trial, continent or year of enrollment as level of clustering were distinctly lower. Less than 1% of the variability of mRS, NIHSS, and BI was explained by any of these three cluster levels. CONCLUSION: This compendium of relevant ICC estimates should assist trial planning. For example, the sample size for a cluster trial with 150 patients per center using ordinal analysis of mRS should be inflated by 2·0 due to the ICC of 0·007; whereas the ICC of 0·031 using mRS dichotomized above mRS 0-1, requires inflation by 5·6. The low contribution of trials, year or continent of enrollment to overall variation in outcome offers reassurance that analyses using pooled data from multiple trials in VISTA are unlikely to suffer from bias from these sources.

Exploration of time-course combinations of outcome scales for use in a global test of stroke recovery.

BACKGROUND: Clinical trials for acute ischemic stroke treatment require large numbers of participants and are expensive to conduct. Methods that enhance statistical power are therefore desirable. AIMS: We explored whether this can be achieved by a measure incorporating both early and late measures of outcome (e.g. seven-day NIH Stroke Scale combined with 90-day modified Rankin scale). METHODS: We analyzed sensitivity to treatment effect, using proportional odds logistic regression for ordinal scales and generalized estimating equation method for global outcomes, with all analyses adjusted for baseline severity and age. We ran simulations to assess relations between sample size and power for ordinal scales and corresponding global outcomes. We used R version 2·12·1 (R Development Core Team. R Foundation for Statistical Computing, Vienna, Austria) for simulations and SAS 9·2 (SAS Institute Inc., Cary, NC, USA) for all other analyses. RESULTS: Each scale considered for combination was sensitive to treatment effect in isolation. The mRS90 and NIHSS90 had adjusted odds ratio of 1·56 and 1·62, respectively. Adjusted odds ratio for global outcomes of the combination of mRS90 with NIHSS7 and NIHSS90 with NIHSS7 were 1·69 and 1·73, respectively. The smallest sample sizes required to generate statistical power ≥80% for mRS90, NIHSS7, and global outcomes of mRS90 and NIHSS7 combined and NIHSS90 and NIHSS7 combined were 500, 490, 400, and 380, respectively. DISCUSSION: When data concerning both early and late outcomes are combined into a global measure, there is increased sensitivity to treatment effect compared with solitary ordinal scales. This delivers a 20% reduction in required sample size at 80% power. Combining early with late outcomes merits further consideration.