Maurer Craig (wallfeast34)
52, 95% confidence interval (CI) 1.27-4.80] when adjusted for confounders. 5-Year overall survival (85% vs 91%, P less then 0.001) and 5-year freedom from major adverse cardiac and cerebrovascular events were also inferior for patients with diabetes (77% vs 82%, P less then 0.001). Cox regression analysis adjusting for potential confounders showed that the diagnosis of diabetes significantly predicted all-cause mortality [hazard ratio (HR) 1.87, 95% CI 1.53-2.29] and increased risk of major adverse cardiac and cerebrovascular events (HR 1.47, 95% CI 1.23-1.75). CONCLUSIONS Patients with diabetes have significantly lower survival after CABG, both within 30 days and during long-term follow-up. © The Author(s) 2020. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.OBJECTIVES Serum CA72-4 levels are elevated in some gout patients but this has not been comprehensively described. The present study profiled serum CA72-4 expression in gout patients and verified the hypothesis that CA72-4 is a predictor of future flares in a prospective gout cohort. METHODS To profile CA72-4 expression, a cross-sectional study was conducted in subjects with gouty arthritis, asymptomatic hyperuricaemia, four major arthritis types (OA, RA, SpA, septic arthritis) and healthy controls. A prospective gout cohort study was initiated to test the value of CA72-4 for predicting gout flares. During a 6-month follow-up, gout flares, CA72-4 levels and other gout-related clinical variables were observed at 1, 3 and 6 months. RESULTS CA72-4 was highly expressed in patients with gouty arthritis [median (interquartile range) 4.55 (1.56, 32.64) U/ml] compared with hyperuricaemia patients [1.47 (0.87, 3.29) U/ml], healthy subjects [1.59 (0.99, 3.39) U/ml] and other arthritis patients [septic arthritis, 1.38 (0.99, 2.66) U/ml; RA, 1.58 (0.95, 3.37) U/ml; SpA, 1.56 (0.98, 2.85) U/ml; OA, 1.54 (0.94, 3.34) U/ml; P 6.9 U/ml) was the strongest predictor of gout flares (hazard ratio = 3.889). Prophylactic colchicine was effective, especially for patients with high CA72-4 levels (P = 0.014). CONCLUSION CA72-4 levels were upregulated in gout patients who experienced frequent flares and CA72-4 was a useful biomarker to predict future flares. © The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology.It has long been recognized that sample size calculations for cluster randomized trials require consideration of the correlation between multiple observations within the same cluster. When measurements are taken at anything other than a single point in time, these correlations depend not only on the cluster but also on the time separation between measurements and additionally, on whether different participants (cross-sectional designs) or the same participants (cohort designs) are repeatedly measured. This is particularly relevant in trials with multiple periods of measurement, such as the cluster cross-over and stepped-wedge designs, but also to some degree in parallel designs. Several papers describing sample size methodology for these designs have been published, but this methodology might not be accessible to all researchers. In this article we provide a tutorial on sample size calculation for cluster randomized designs with particular emphasis on designs with multiple periods of measurement and provide a web-based tool, the Shiny CRT Calculator, to allow researchers to easily conduct these sample size calculations. We consider both cross-sectional and cohort designs and allow for a variety of assumed within-cluster correlation structures. We consider cluster heterogeneity in treatment effects (for designs where treatment is crossed with cluster), as well as individually randomized group-treatment trials with differential clustering between arms, for example designs where clustering arises from interventions being delivered in groups. The calculator w
