Cardiovascular risk factor control and outcomes in peripheral artery disease patients in the Reduction of Atherothrombosis for Continued Health (REACH) Registry

OBJECTIVES: To examine differences in risk factor (RF) management between peripheral artery disease (PAD) and coronary artery (CAD) or cerebrovascular disease (CVD), as well as the impact of RF control on major 1-year cardiovascular (CV) event rates. METHODS: The REACH Registry recruited >68000 outpatients aged >/=45 years with established atherothrombotic disease or >/=3 RFs for atherothrombosis. The predictors of RF control that were evaluated included: (1) patient demographics, (2) mode of PAD diagnosis, and (3) concomitant CAD and/or CVD. RESULTS: RF control was less frequent in patients with PAD (n=8322), compared with those with CAD or CVD (but no PAD, n=47492) [blood pressure; glycemia; total cholesterol; smoking cessation (each P<0.001)]. Factors independently associated with optimal RF control in patients with PAD were male gender (OR=1.9); residence in North America (OR=3.5), Japan (OR=2.5) or Latin America (OR=1.5); previous coronary revascularization (OR=1.3); and statin use (OR=1.4); whereas prior leg amputation was a negative predictor (OR=0.7) (P<0.001). Optimal RF control was associated with fewer 1-year CV ischemic symptoms or events. CONCLUSIONS: Patients with PAD do not achieve RF control as frequently as individuals with CAD or CVD. Improved RF control is associated with a positive impact on 1-year CV event rates.

Placebo effect characteristics observed in a single, international, longitudinal study in Huntington's disease

BACKGROUND Classically, clinical trials are based on the placebo-control design. Our aim was to analyze the placebo effect in Huntington's disease. METHODS Placebo data were obtained from an international, longitudinal, placebo-controlled trial for Huntington's disease (European Huntington's Disease Initiative Study Group). One-hundred and eighty patients were evaluated using the Unified Huntington Disease Rating Scale over 36 months. A placebo effect was defined as an improvement of at least 50% over baseline scores in the Unified Huntington Disease Rating Scale, and clinically relevant when at least 10% of the population met it. RESULTS Only behavior showed a significant placebo effect, and the proportion of the patients with placebo effect ranged from 16% (first visit) to 41% (last visit). Nondepressed patients with better functional status were most likely to be placebo-responders over time. CONCLUSIONS In Huntington's disease, behavior seems to be more vulnerable to placebo than overall motor function, cognition, and function

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.