Ascertainment of cause-specific mortality in COPD - operations of the TORCH clinical endpoint Committee

Background: TORCH (Towards a Revolution in COPD Health) is an international multicentre, randomised, placebo-controlled clinical trial of inhaled fluticasone propionate/salmeterol combination treatment and its monotherapy components for maintenance treatment of moderately to severely impaired patients with chronic obstructive pulmonary disease (COPD). The primary outcome is all-cause mortality. Cause-specific mortality and deaths related to COPD are additional outcome measures, but systematic methods for ascertainment of these outcomes have not previously been described. Methods: A Clinical Endpoint Committee (CEC) was tasked with categorising the cause of death and the relationship of deaths to COPD in a systematic, unbiased and independent manner. The key elements of the operation of the committee were the use of predefined principles of operation and definitions of cause of death and COPD-relatedness; the independent review of cases by all members with development of a consensus opinion; and a substantial infrastructure to collect medical information. Results: 911 deaths were reviewed and consensus was reached in all. Cause-specific mortality was: cardiovascular 27%, respiratory 35%, cancer 21%, other 10% and unknown 8%. 40% of deaths were definitely or probably related to COPD. Adjudications were identical in 83% of blindly re-adjudicated cases ( = 0.80). COPD-relatedness was reproduced 84% of the time ( = 0.73). The CEC adjudication was equivalent to the primary cause of death recorded by the site investigator in 52% of cases. Conclusion: A CEC can provide standardised, reliable and informative adjudication of COPD mortality that provides information which frequently differs from data collected from assessment by site investigators.

Fast model based feature matching technique applied to airport lighting

The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting. The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade–Lucus–Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.