18 research outputs found

    Meta-analysis of individual-patient data from EVAR-1, DREAM, OVER and ACE trials comparing outcomes of endovascular or open repair for abdominal aortic aneurysm over 5 years

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    Background: The erosion of the early mortality advantage of elective endovascular aneurysm repair (EVAR) compared with open repair of abdominal aortic aneurysm remains without a satisfactory explanation. Methods: An individual-patient data meta-analysis of four multicentre randomized trials of EVAR versus open repair was conducted to a prespecified analysis plan, reporting on mortality, aneurysm-related mortality and reintervention. Results: The analysis included 2783 patients, with 14 245 person-years of follow-up (median 5·5 years). Early (0–6 months after randomization) mortality was lower in the EVAR groups (46 of 1393 versus 73 of 1390 deaths; pooled hazard ratio 0·61, 95 per cent c.i. 0·42 to 0·89; P = 0·010), primarily because 30-day operative mortality was lower in the EVAR groups (16 deaths versus 40 for open repair; pooled odds ratio 0·40, 95 per cent c.i. 0·22 to 0·74). Later (within 3 years) the survival curves converged, remaining converged to 8 years. Beyond 3 years, aneurysm-related mortality was significantly higher in the EVAR groups (19 deaths versus 3 for open repair; pooled hazard ratio 5·16, 1·49 to 17·89; P = 0·010). Patients with moderate renal dysfunction or previous coronary artery disease had no early survival advantage under EVAR. Those with peripheral artery disease had lower mortality under open repair (39 deaths versus 62 for EVAR; P = 0·022) in the period from 6 months to 4 years after randomization. Conclusion: The early survival advantage in the EVAR group, and its subsequent erosion, were confirmed. Over 5 years, patients of marginal fitness had no early survival advantage from EVAR compared with open repair. Aneurysm-related mortality and patients with low ankle : brachial pressure index contributed to the erosion of the early survival advantage for the EVAR group. Trial registration numbers: EVAR-1, ISRCTN55703451; DREAM (Dutch Randomized Endovascular Aneurysm Management), NCT00421330; ACE (Anévrysme de l'aorte abdominale, Chirurgie versus Endoprothèse), NCT00224718; OVER (Open Versus Endovascular Repair Trial for Abdominal Aortic Aneurysms), NCT00094575

    Reviewing the use of resilience concepts in forest sciences

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    Purpose of the review Resilience is a key concept to deal with an uncertain future in forestry. In recent years, it has received increasing attention from both research and practice. However, a common understanding of what resilience means in a forestry context, and how to operationalise it is lacking. Here, we conducted a systematic review of the recent forest science literature on resilience in the forestry context, synthesising how resilience is defined and assessed. Recent findings Based on a detailed review of 255 studies, we analysed how the concepts of engineering resilience, ecological resilience, and social-ecological resilience are used in forest sciences. A clear majority of the studies applied the concept of engineering resilience, quantifying resilience as the recovery time after a disturbance. The two most used indicators for engineering resilience were basal area increment and vegetation cover, whereas ecological resilience studies frequently focus on vegetation cover and tree density. In contrast, important social-ecological resilience indicators used in the literature are socio-economic diversity and stock of natural resources. In the context of global change, we expected an increase in studies adopting the more holistic social-ecological resilience concept, but this was not the observed trend. Summary Our analysis points to the nestedness of these three resilience concepts, suggesting that they are complementary rather than contradictory. It also means that the variety of resilience approaches does not need to be an obstacle for operationalisation of the concept. We provide guidance for choosing the most suitable resilience concept and indicators based on the management, disturbance and application context

    Automated mapping of relict patterned ground: An approach to evaluate morphologically subdued landforms using unmanned-aerial-vehicle and structure-from-motion technologies

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    Relict landforms provide a wealth of information on the evolution of the modern landscape and climate change in the past. To improve understanding of the origin and development of these landforms we need better spatial measurements across a variety of scales. This can be challenging using conventional surveying techniques due to difficulties in landform recognition on the ground (e.g. weak visual/topographic expression) and spatially variable areas of interest. Here we explore the appropriateness of existing remote sensing datasets (aerial LiDAR and aerial photography) and newly acquired unmanned aerial vehicle (UAV) imagery of a test site on the upland of Dartmoor in SW England (Leeden Tor) for the recognition and automated mapping of relict patterned ground composed of stripes and polygons. We find that the recognition of these landforms is greatly enhanced by automated mapping using spectral two-dimensional imagery. Image resolution is important, with the recognition of elements (boulders) of &lt;1 m maximised from the highest resolution imagery (UAV red-green-blue (RGB)) and recognition of landforms (10–100 m scale) maximised on coarser resolution aerial imagery. Topographic metrics of these low relief (0.5 m) landforms are best extracted from structure-from-motion (SfM) processed UAV true-colour imagery, and in this context the airborne LiDAR data proved less effective. Integrating automated mapping using spectral attributes and SfM-derived digital surface models from UAV RGB imagery provides a powerful tool for rapid reconnaissance of field sites to facilitate the extraction of meaningful topographic and spatial metrics that can inform on the origin of relict landform features. Care should be given to match the scale of features under consideration to the appropriate scale of datasets available.</jats:p
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