The American Thoracic Society/European Respiratory Society jointly created a Task Force on "Outcomes for COPD pharmacological trials: from lung function to biomarkers" to inform the chronic obstructive pulmonary disease research community about the possible use and limitations of current outcomes and markers when evaluating the impact of a pharmacological therapy. Based on their review of the published literature, the following document has been prepared with individual sections that address specific outcomes and markers, and a final section that summarises their recommendations.

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Outcomes for COPD pharmacological trials: From lung function to biomarkers

To develop evidence-based recommendations for the use of imaging modalities in primary large vessel vasculitis (LVV) including giant cell arteritis (GCA) and Takayasu arteritis (TAK). European League Against Rheumatism (EULAR) standardised operating procedures were followed. A systematic literature review was conducted to retrieve data on the role of imaging modalities including ultrasound, MRI, CT and [18F]-fluorodeoxyglucose positron emission tomography (PET) in LVV. Based on evidence and expert opinion, the task force consisting of 20 physicians, healthcare professionals and patients from 10 EULAR countries developed recommendations, with consensus obtained through voting. The final level of agreement was voted anonymously. A total of 12 recommendations have been formulated. The task force recommends an early imaging test in patients with suspected LVV, with ultrasound and MRI being the first choices in GCA and TAK, respectively. CT or PET may be used alternatively. In case the diagnosis is still in question after clinical examination and imaging, additional investigations including temporal artery biopsy and/or additional imaging are required. In patients with a suspected flare, imaging might help to better assess disease activity. The frequency and choice of imaging modalities for long-term monitoring of structural damage remains an individual decision; close monitoring for aortic aneurysms should be conducted in patients at risk for this complication. All imaging should be performed by a trained specialist using appropriate operational procedures and settings. These are the first EULAR recommendations providing up-to-date guidance for the role of imaging in the diagnosis and monitoring of patients with (suspected) LVV.

https://ard.bmj.com/content/annrheumdis/77/5/636.full.pdf

EULAR recommendations for the use of imaging in large vessel vasculitis in clinical practice.

Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context.

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A conceptual view on trajectories

Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for: (i) constructing trajectories from movement tracks, (ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and (iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the article surveys the new privacy issues that arise due to the semantic aspects of trajectories.

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Semantic trajectories modeling and analysis

Expansion of the prognostic assessment of patients with chronic obstructive pulmonary disease: the updated BODE index and the ADO index

Technological advances in position‐aware devices are leading to a wealth of data documenting motion. The integration of spatio‐temporal data‐mining techniques in GIScience is an important research field to overcome the limitations of static Geographic Information Systems with respect to the emerging volumes of data describing dynamics. This paper presents a generic geographic knowledge discovery approach for exploring the motion of moving point objects, the prime modelling construct to represent GPS tracked animals, people, or vehicles. The approach is based on the concept of geospatial lifelines and presents a formalism for describing different types of lifeline patterns that are generalizable for many application domains. Such lifeline patterns allow the identification and quantification of remarkable individual motion behaviour, events of distinct group motion behaviour, so as to relate the motion of individuals to groups. An application prototype featuring novel data‐mining algorithms has been impleme...

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Discovering relative motion patterns in groups of moving point objects

Technological advances in position aware devices increase the availability of tracking data of everyday objects such as animals, vehicles, people or football players. We propose a geographic data mining approach to detect generic aggregation patterns such as flocking behaviour and convergence in geospatial lifeline data. Our approach considers the object’s motion properties in an analytical space as well as spatial constraints of the object’s lifelines in geographic space. We discuss the geometric properties of the formalised patterns with respect to their efficient computation.

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Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

The overall goal of the ongoing project is to develop methods for spatio-temporal analysis of relative motion within groups of moving point objects, such as GPS-tracked animals. Whereas recent efforts of dealing with dynamic phenomena within the GIScience community mainly concentrated on modeling and representation, this research project concentrates on the analytic task. The analysis is performed on a process level and does not use the traditional cartographic approach of comparing snapshots. The analysis concept called REMO (RElative MOtion) is based on the comparison of motion parameters of objects over time. Therefore the observation data is transformed into a 2.5-dimensional analysis matrix, featuring a time axis, an object axis and motion parameters. This matrix reveals basic searchable relative movement patterns. The current approach handles points in a pure featureless space. Case study data of GPS-observed animals and political entities in an ideological space are used for illustration purposes.

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Analyzing Relative Motion within Groups of Trackable Moving Point Objects

Objectives The aim of this study was to investigate the relationship between rheumatoid arthritis (RA) and the occurrence of diabetes mellitus (DM). Methods A meta-analysis was conducted to explore the risk of DM in RA patients. All relevant studies were identified by searching PUBMED, EMBASE and MEDLINE database prior to 1 January 2014. Pooled risk estimates were calculated with random-effects models using STATA 11.0. Results A total of 11 case-control studies and 8 cohort studies were included in the final analysis. The pooled risk estimate of 11 case-control studies showed a statistically significant increased risk of DM prevalence among RA individuals (OR=1.40, 95% CI: 1.34-1.47). The pooled risk estimate of 8 cohort studies also showed a statistically significant increasing risk of DM (RR=1.43, 95%CI: 1.38-1.47). In a subgroup analysis for case-control studies, the pooled risk estimate of individuals with RA increased the incidence of T1DM (type 1 diabetes mellitus) and the incidence of T2DM (type 2 diabetes mellitus) (OR, 4.78 vs. 1.41). In a subgroup analysis for cohort studies, RA was also found to have a statistically significant increasing risk of T2DM (RR=1.24, 95%CI: 1.14-1.35). Begg funnel plot and Egger test showed no evidence of publication bias. Conclusions RA is associated with increased risk of DM, including T1DM and T2DM.

Stephan Imfeld

Papers

Discovering relative motion patterns in groups of moving point objects

Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

Analyzing Relative Motion within Groups of Trackable Moving Point Objects

The ultrasound compression sign to diagnose temporal giant cell arteritis shows an excellent interobserver agreement.

The BODE index after lung volume reduction surgery correlates with survival.