Long-term Results of Totally Laparoscopic Aortobifemoral Bypass

OBJECTIVES: The aim was to estimate the long-term results and patency rates of totally laparoscopic aortobifemoral bypass in aorto-iliac occlusive disease (AIOD). METHODS: All 87 patients who received a laparoscopic aortobifemoral bypass for AIOD on an intention to treat basis between October 2003 and October 2013 were identified. All operations were performed by the same surgical team using a totally laparoscopic technique. Demographic, pre-operative, peri-operative, and follow up variables were collected and analyzed. Patients were followed up at 1 month post-operatively and annually thereafter. Patency rates were calculated in accordance with published patency reporting standards. RESULTS: The median age was 57 years (range 40-78 years). The conversion rate was 20.6% overall. The thirty-day post-operative mortality was 1.1%. Six patients required early re-intervention. There were no graft infections. The median length hospital stay was 6 days (range 4-39 days). The mean follow up was 58.0 months (range 1-133 months). Graft limb based primary, primary assisted, and secondary patency rates were respectively 96.1%, 98.1% and 99.4% at 1 year, and 83.0%, 92.0% and 97.0% at 5 years. CONCLUSION: Totally laparoscopic aortobifemoral bypass is a safe alternative to open surgery in selected patients, with excellent long-term patency rates, albeit at the cost of a steep learning curve.status: publishe

Gradient Art: Creation and Vectorization

There are two different categories of methods for producing vector gradients. One is mainly interested in converting existing photographs into dense vector representations. By vector it is meant that one can zoom infinitely inside images, and that control values do not have to lie onto a grid but must represent subtle color gradients found in input images. The other category is tailored to the creation of images from scratch, using a sparse set of vector primitives. In this case, we still have the infinite zoom property, but also an advanced model of how space should be filled in-between primitives, since there is no input photograph to rely on. These two categories are actually extreme cases, and seem to exclude each other: a dense representation is difficult to manipulate, especially when one wants to modify topology; a sparse representation is hardly adapted to photo vectorization, especially in the presence of texture. Very few methods lie in the middle, and the ones that do require user assistance. The challenge is worth the effort though: it would make converting an image into vector primitives easily amenable to stylization