Tissue coverage of a hydrophilic polymer-coated zotarolimus-eluting stent vs. a fluoropolymer-coated everolimus-eluting stent at 13-month follow-up: an optical coherence tomography substudy from the RESOLUTE All Comers trial

Aims To compare the tissue coverage of a hydrophilic polymer-coated zotarolimus-eluting stent (ZES) vs. a fluoropolymer-coated everolimus-eluting stent (EES) at 13 months, using optical coherence tomography (OCT) in an ‘all-comers' population of patients, in order to clarify the mechanism of eventual differences in the biocompatibility and thrombogenicity of the devices. Methods and results Patients randomized to angiographic follow-up in the RESOLUTE All Comers trial (NCT00617084) at pre-specified OCT sites underwent OCT follow-up at 13 months. Tissue coverage and apposition were assessed strut by strut, and the results in both treatment groups were compared using multilevel logistic or linear regression, as appropriate, with clustering at three different levels: patient, lesion, and stent. Fifty-eight patients (30 ZES and 28 EES), 72 lesions, 107 stents, and 23 197 struts were analysed. Eight hundred and eighty-seven and 654 uncovered struts (7.4 and 5.8%, P= 0.378), and 216 and 161 malapposed struts (1.8 and 1.4%, P= 0.569) were found in the ZES and EES groups, respectively. The mean thickness of coverage was 116 ± 99 µm in ZES and 142 ± 113 µm in EES (P= 0.466). No differences in per cent neointimal volume obstruction (12.5 ± 7.9 vs. 15.0 ± 10.7%) or other areas–volumetric parameters were found between ZES and EES, respectively. Conclusion No significant differences in tissue coverage, malapposition, or lumen/stent areas and volumes were detected by OCT between the hydrophilic polymer-coated ZES and the fluoropolymer-coated EES at 13-month follow-up.

AccessMod 3.0: computing geographic coverage and accessibility to health care services using anisotropic movement of patients

Background Access to health care can be described along four dimensions: geographic accessibility, availability, financial accessibility and acceptability. Geographic accessibility measures how physically accessible resources are for the population, while availability reflects what resources are available and in what amount. Combining these two types of measure into a single index provides a measure of geographic (or spatial) coverage, which is an important measure for assessing the degree of accessibility of a health care network. Results This paper describes the latest version of AccessMod, an extension to the Geographical Information System ArcView 3.×, and provides an example of application of this tool. AccessMod 3 allows one to compute geographic coverage to health care using terrain information and population distribution. Four major types of analysis are available in AccessMod: (1) modeling the coverage of catchment areas linked to an existing health facility network based on travel time, to provide a measure of physical accessibility to health care; (2) modeling geographic coverage according to the availability of services; (3) projecting the coverage of a scaling-up of an existing network; (4) providing information for cost effectiveness analysis when little information about the existing network is available. In addition to integrating travelling time, population distribution and the population coverage capacity specific to each health facility in the network, AccessMod can incorporate the influence of landscape components (e.g. topography, river and road networks, vegetation) that impact travelling time to and from facilities. Topographical constraints can be taken into account through an anisotropic analysis that considers the direction of movement. We provide an example of the application of AccessMod in the southern part of Malawi that shows the influences of the landscape constraints and of the modes of transportation on geographic coverage. Conclusion By incorporating the demand (population) and the supply (capacities of heath care centers), AccessMod provides a unifying tool to efficiently assess the geographic coverage of a network of health care facilities. This tool should be of particular interest to developing countries that have a relatively good geographic information on population distribution, terrain, and health facility locations.

Assessing vegetation cover and biomass in restored erosion areas in Iceland using SPOT satellite data

Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs. Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R2 and RMSE values for most VIs do not vary very much. For percent VC, R2 values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R2 values for low-biomass areas (AGB < 800 g/m2) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m2 and 136.38 g/m2. The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m2), achieved R2 values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m2 to 259.20 g/m2. The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced. These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m2 can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.