Midwall fibrosis is an independent predictor of mortality and morbidity in patients with DCM undergoing CRT. The outcome of DCM with midwall fibrosis is similar to that of ICM. This relationship is mediated by both pump failure and sudden cardiac death.

Background With the epidemic of childhood obesity, there is national interest in state-level school policies related to nutrition and physical activity, policies adopted by states, and relationships to youth obesity. Purpose This study develops a comprehensive state-level approach to characterize the overall obesity prevention policy environment for schools and links the policy environments to youth obesity for each state. Methods Using 2006 School Health Policies and Programs Study (SHPPS) state data, qualitative and quantitative methods were used (2008–2009) to construct domains of state-level school obesity prevention policies and practices, establish the validity and reliability of the domain scales, and examine their associations with state-level obesity prevalence among youth aged 10–17 years from the 2003 National Survey of Children’s Health. Results Nearly 250 state-level obesity prevention–policy questions were identified from the SHPPS. Three broad policy topic areas containing 100 food service and nutrition (FSN) questionnaire items; 146 physical activity and education (PAE) items; and two weight assessment (WA) items were selected. Principal components analysis and content validity assessment were used to further categorize the items into six FSN, ten PAE, and one WA domain. Using a proportional scaled score to summarize the number of policies adopted by states, it was found that on average states adopted about half of the FSN (49%), 38% of the PAE, and 17% of the WA policies examined. After adjusting for state-level measures of ethnicity and income, the average proportion of FSN policies adopted by states was correlated with the prevalence of youth obesity at r =0.35 (p=0.01). However, no correlation was found between either PAE or WA policies and youth obesity (PAE policies at r=0.02 [p=0.53] and WA policies at r =0.16 [p=0.40]). Conclusions States appear to be doing a better job adopting FSN policies than PA or WA policies, and adoption of policies is correlated with youth obesity. Continued monitoring of these policies seems to be warranted.

In silico clinical trials, defined as ÒThe use of individualised computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical interventionÓ, have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognised as inadequate, as for example the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the Padova Ð UVA simulator that the FDA has accepted as possible replacement for animal testing in the pre-clinical assessment of artificial pancreas technologies, and the second an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patientsÕ phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.

Evaluation of medical devices via clinical trial is often a necessary step in the process of bringing a new product to market. In recent years, device manufacturers are increasingly using stochastic engineering models during the product development process. These models have the capability to simulate virtual patient outcomes. This article presents a novel method based on the power prior for augmenting a clinical trial using virtual patient data. To properly inform clinical evaluation, the virtual patient model must simulate the clinical outcome of interest, incorporating patient variability, as well as the uncertainty in the engineering model and in its input parameters. The number of virtual patients is controlled by a discount function which uses the similarity between modeled and observed data. This method is illustrated by a case study of cardiac lead fracture. Different discount functions are used to cover a wide range of scenarios in which the type I error rates and power vary for the same number of enrolled patients. Incorporation of engineering models as prior knowledge in a Bayesian clinical trial design can provide benefits of decreased sample size and trial length while still controlling type I error rate and power.

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