Safety of nifedipine GITS in stable angina: The ACTION trial

Aim: We describe the safety profile of nifedipine GITS as assessed from adverse events reported in the ACTION trial in which 7,665 patients with stable, symptomatic coronary artery disease were randomly assigned nifedipine GITS or placebo and followed for a mean of 4.9 years. Methods: All adverse events were coded using the COSTART coding dictionary. The incidence rate for each event was calculated as the number of patients with the event concerned divided by the total time 'at risk'. Hazard ratios comparing nifedipine with placebo and their 95% confidence intervals were obtained by Cox proportional-hazards analysis. Results: As reported previously, nifedipine significantly reduced the incidence of cardiovascular events and procedures [hazard ratio (HR) 0.89, 95% confidence interval (CI) 0.83-0.95]. Apart from the known side effects of nifedipine, which include peripheral oedema, vasodilatation, hypotension, asthenia, constipation, leg cramps, non-specific respiratory complaints, impotence and polyuria, and which were reported more frequently in patients assigned nifedipine, the incidence rates of most other adverse events were similar. There were no differences in the occurrence of gastrointestinal haemorrhage, myocardial infarction and suicide. The rate of occurrence of death or new cancer excluding non-melanoma skin cancer for patients with no history of cancer at baseline was 2.53/100 patient years for patients assigned nifedipine and 2.37/100 patient years for patients assigned placebo (HR 1.06, 95% CI 0.93-1.22). Conclusion: Overall nifedipine GITS was well tolerated by patients with stable symptomatic angina

Economic Regime Identification and Prediction in TAC SCM Using Sales and Procurement Information

Our research is focused on the effects of the additionof procurement information (offer prices) to a sales-based economic regime model. This model is used for strategic, tactical, and operational decision making in dynamic supply chains. We evaluate the performance of the regime model through experiments with the MinneTAC trading agent, which competes in the TAC SCM game. The new regime model has an overall predictive performance which is equal to the performance of the existing model. Regime switches are predicted more accurately, whereas the prediction accuracy of dominant regimes does not improve. However, because procurement information has been added to the model, the model has been enriched, which gives new opportunities for applications in the procurement market, such as procurement reserve pricing

Resource utilization implications of treatment were able to be assessed from appropriately reported clinical trial data

Background and Objective: Published clinical trial data rarely allow assessment of the health care resource utilization implications of treatment. We give an example of how these can be assessed given appropriate tabulation of data. Methods: Data from a trial comparing long-acting nifedipine gastrointestinal therapeutic system to placebo in 7,665 patients with stable angina pectoris was analyzed. Results: Relative to placebo, nifedipine significantly increased mean cardiovascular (CV) event-free survival by 41 days but had no effect on mean survival. Per 100 years of follow-up, 78.1 patient-years of double-blind nifedipine administration reduced use of another calcium antagonist, an angiotensin converting enzyme inhibitor, an angiotensin receptor blocker, a diuretic and a cardiac glycoside by 1.54, 3.73, 2.63, 2.23, and 0.64 years, respectively, whereas 0.21 less hospitalization for overt heart failure, 0.47 less hospitalization for any stroke or transient ischemic attack, 0.8 less coronary angiogram, 0.38 less coronary bypass procedure, and 0.13 additional orthopedic procedure was required. Combining resource utilization with cost data for one particular hospital showed that one additional year of CV event-free survival costs an average additional EURO3,036 in the setting considered. Conclusion: Appropriately tabulated clinical trial data allows clinicians to judge the resource utilization implications and economic effect of treatment decisions. (c) 2007 Elsevier Inc. All rights reserved

Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)

Introduction: The segmentation and volumetric quantification of white matter (WM) lesions play an important role in monitoring and studying neurological diseases such as multiple sclerosis (MS) or cerebrovascular disease. This is often interactively done using 2D magnetic resonance images. Recent developments in acquisition techniques allow for 3D imaging with much thinner sections, but the large number of images per subject makes manual lesion outlining infeasible. This warrants the need for a reliable automated approach. Here we aimed to improve k nearest neighbor (kNN) classification of WM lesions by optimizing intensity normalization and using spatial tissue type priors (TTPs). Methods: The kNN-TTP method used kNN classification with 3.0T 3DFLAIR and 3DT1 intensities as well as MNI-normalized spatial coordinates as features. Additionally, TTPs were computed by nonlinear registration of data from healthy controls. Intensity features were normalized using variance scaling, robust range normalization or histogram matching. The algorithm was then trained and evaluated using a leave-one-out experiment among 20 patients with MS against a reference segmentation that was created completely manually. The performance of each normalization method was evaluated both with and without TTPs in the feature set. Volumetric agreement was evaluated using intra-class coefficient (ICC), and voxelwise spatial agreement was evaluated using Dice similarity index (SI). Finally, the robustness of the method across different scanners and patient populations was evaluated using an independent sample of elderly subjects with hypertension. Results: The intensity normalization method had a large influence on the segmentation performance, with average SI values ranging from 0.66 to 0.72 when no TTPs were used. Independent of the normalization method, the inclusion of TTPs as features increased performance particularly by reducing the lesion detection error. Best performance was achieved using variance scaled intensity features and including TTPs in the feature set: this yielded ICC = 0.93 and average SI = 0.75 +/- 0.08. Validation of the method in an independent sample of elderly subjects with hypertension, yielded even higher ICC = 0.96 and SI = 0.84 +/- 0.14. Conclusion: Adding TTPs increases the performance of kNN based MS lesion segmentation methods. Best performance was achieved using variance scaling for intensity normalization and including TTPs in the feature set, showing excellent agreement with the reference segmentations across a wide range of lesion severity, irrespective of the scanner used or the pathological substrate of the lesions. (C) 2013 The Authors. Published by Elsevier In