The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Ramucirumab plus docetaxel versus placebo plus docetaxel in patients with locally advanced or metastatic urothelial carcinoma after platinum-based therapy (RANGE): a randomised, double-blind, phase 3 trial

Few treatments with a distinct mechanism of action are available for patients with platinum-refractory advanced or metastatic urothelial carcinoma. We assessed the efficacy and safety of treatment with docetaxel plus either ramucirumab-a human IgG1 VEGFR-2 antagonist-or placebo in this patient population

From a user study to a valid claim:how to test your hypothesis and avoid common pitfalls

The evaluation of visualization methods or designs often relies on user studies. Apart from the difficulties involved in the design of the study itself, the existing mechanisms to obtain sound conclusions are often unclear. In this work, we review and summarize some of the common statistical techniques that can be used to validate a claim in the scenarios that are commonly present in user studies in visualization, i.e., hypothesis testing. Usually, the number of participants is small and the mean and variance of the distribution are not known. Therefore, we will focus on the techniques that are adequate within these limitations. Our aim for this paper is to clarify the goals and limitations of hypothesis testing from a user study perspective, that can be interesting for the visualization community. We provide an overview of the most common mistakes made when testing a hypothesis that can lead to erroneous claims. We also present strategies to avoid those

Temporal interpolation of 4D PC-MRI blood-flow measurements using bidirectional physics-based fluid simulation

Magnetic Resonance Imaging (MRI) enables volumetric and time-varying measurements of blood-flow data. Such data have shown potential to improve diagnosis and risk assessment of various cardiovascular diseases. Hereby, a unique way of analysing patient-specific haemodynamics becomes possible. However, these measurements are susceptible to artifacts, noise and a coarse spatio-temporal resolution. Furthermore, typical flow visualization techniques rely on interpolation. For example, using pathlines requires a high quality temporal resolution. While numerical simulations, based on mathematical flow models, address some of these limitations, the involved modelling assumptions (e.g., regarding the inflow and mesh) do not provide patientspecific data to the degree actual measurements would. To overcome this issue, data assimilation techniques can be applied to use measured data in order to steer a physically-based simulation of the flow, combining the benefits of measured data and simulation. Our work builds upon such an existing solution to increase the temporal resolution of the measured data, but achieves significantly higher fidelity. We avoid the previous damping and interpolation bias towards one of the measurements, by simulating bidrectionally (forwards and backwards through time) and using sources and sinks. Our method is evaluated and compared to the, currently-used, conventional interpolation scheme and forward-only simulation using measured and analytical flow data. It reduces artifacts, noise, and interpolation error, while being closer to laminar flow, as is expected for flow in vessels

From a user study to a valid claim: how to test your hypothesis and avoid common pitfalls

The evaluation of visualization methods or designs often relies on user studies. Apart from the difficulties involved in the design of the study itself, the existing mechanisms to obtain sound conclusions are often unclear. In this work, we review and summarize some of the common statistical techniques that can be used to validate a claim in the scenarios that are commonly present in user studies in visualization, i.e., hypothesis testing. Usually, the number of participants is small and the mean and variance of the distribution are not known. Therefore, we will focus on the techniques that are adequate within these limitations. Our aim for this paper is to clarify the goals and limitations of hypothesis testing from a user study perspective, that can be interesting for the visualization community. We provide an overview of the most common mistakes made when testing a hypothesis that can lead to erroneous claims. We also present strategies to avoid those

A framework for fast initial exploration of PC-MRI cardiac flow

Cardiac flow is still not fully understood, and is currently an active research topic. Using phase-contrast magnetic resonance imaging (PC-MRI) blood flow can be measured. For the inspection of such flow, researchers often rely on methods that require additional scans produced by different imaging modalities to provide context. This requires labor-intensive registration and often manual segmentation before any exploration of the data is performed. This work provides a framework that allows for a quick exploration of cardiac flow without the need of additional imaging and time-consuming segmentation. To achieve this, only the 4D data from one PC-MRI scan is used. A context visualization is derived automatically from the data, and provides context for the flow. Instead of relying on segmentation to deliver an accurate context, the heart's ventricles are approximated by half-ellipsoids that can be placed with minimal user interaction. Furthermore, seeding positions for flow visualization can be placed automatically in areas of interest defined by the user and based on derived flow features. The framework enables a user to do a fast initial exploration of cardiac flow, as is demonstrated by a use case and a user study involving cardiac blood flow researchers

InkVis:a high-particle-count approach for visualization of phase-contrast magnetic resonance imaging data

Phase-Contrast Magnetic Resonance Imaging (PC-MRI) measures volumetric and time-varying blood flow data, unsurpassed in quality and completeness. Such blood-flow data have been shown to have the potential to improve both diagnosis and risk assessment of cardiovascular diseases (CVDs) uniquely. Typically PC-MRI data is visualized using stream- or pathlines. However, time-varying aspects of the data, e.g., vortex shedding, breakdown, and formation, are not sufficiently captured by these visualization techniques. Experimental flow visualization techniques introduce a visible medium, like smoke or dye, to visualize flow aspects including time-varying aspects. We propose a framework that mimics such experimental techniques by using a high number of particles. The framework offers great flexibility which allows for various visualization approaches. These include common traditional flow visualizations, but also streak visualizations to show the temporal aspects, and uncertainty visualizations. Moreover, these patient-specific measurements suffer from noise artifacts and a coarse resolution, causing uncertainty. Traditional flow visualizations neglect uncertainty and, therefore, may give a false sense of certainty, which can mislead the user yielding incorrect decisions. Previously, the domain experts had no means to visualize the effect of the uncertainty in the data. Our framework has been adopted by domain experts to visualize the vortices present in the sinuses of the aorta root showing the potential of the framework. Furthermore, an evaluation among domain experts indicated that having the option to visualize the uncertainty contributed to their confidence on the analysis

InkVis: a high-particle-count approach for visualization of phase-contrast magnetic resonance imaging data

Phase-Contrast Magnetic Resonance Imaging (PC-MRI) measures volumetric and time-varying blood flow data, unsurpassed in quality and completeness. Such blood-flow data have been shown to have the potential to improve both diagnosis and risk assessment of cardiovascular diseases (CVDs) uniquely. Typically PC-MRI data is visualized using stream- or pathlines. However, time-varying aspects of the data, e.g., vortex shedding, breakdown, and formation, are not sufficiently captured by these visualization techniques. Experimental flow visualization techniques introduce a visible medium, like smoke or dye, to visualize flow aspects including time-varying aspects. We propose a framework that mimics such experimental techniques by using a high number of particles. The framework offers great flexibility which allows for various visualization approaches. These include common traditional flow visualizations, but also streak visualizations to show the temporal aspects, and uncertainty visualizations. Moreover, these patient-specific measurements suffer from noise artifacts and a coarse resolution, causing uncertainty. Traditional flow visualizations neglect uncertainty and, therefore, may give a false sense of certainty, which can mislead the user yielding incorrect decisions. Previously, the domain experts had no means to visualize the effect of the uncertainty in the data. Our framework has been adopted by domain experts to visualize the vortices present in the sinuses of the aorta root showing the potential of the framework. Furthermore, an evaluation among domain experts indicated that having the option to visualize the uncertainty contributed to their confidence on the analysis

A framework for fast initial exploration of PC-MRI cardiac flow

Cardiac flow is still not fully understood, and is currently an active research topic. Using phase-contrast magnetic resonance imaging (PC-MRI) blood flow can be measured. For the inspection of such flow, researchers often rely on methods that require additional scans produced by different imaging modalities to provide context. This requires labor-intensive registration and often manual segmentation before any exploration of the data is performed. This work provides a framework that allows for a quick exploration of cardiac flow without the need of additional imaging and time-consuming segmentation. To achieve this, only the 4D data from one PC-MRI scan is used. A context visualization is derived automatically from the data, and provides context for the flow. Instead of relying on segmentation to deliver an accurate context, the heart's ventricles are approximated by half-ellipsoids that can be placed with minimal user interaction. Furthermore, seeding positions for flow visualization can be placed automatically in areas of interest defined by the user and based on derived flow features. The framework enables a user to do a fast initial exploration of cardiac flow, as is demonstrated by a use case and a user study involving cardiac blood flow researchers

A framework for fast initial exploration of PC-MRI cardiac flow

Cardiac flow is still not fully understood, and is currently an active research topic. Using phase-contrast magnetic resonance imaging (PC-MRI) blood flow can be measured. For the inspection of such flow, researchers often rely on methods that require additional scans produced by different imaging modalities to provide context. This requires labor-intensive registration and often manual segmentation before any exploration of the data is performed. This work provides a framework that allows for a quick exploration of cardiac flow without the need of additional imaging and time-consuming segmentation. To achieve this, only the 4D data from one PC-MRI scan is used. A context visualization is derived automatically from the data, and provides context for the flow. Instead of relying on segmentation to deliver an accurate context, the heart's ventricles are approximated by half-ellipsoids that can be placed with minimal user interaction. Furthermore, seeding positions for flow visualization can be placed automatically in areas of interest defined by the user and based on derived flow features. The framework enables a user to do a fast initial exploration of cardiac flow, as is demonstrated by a use case and a user study involving cardiac blood flow researchers