294 Implementation time of a lipid lowering therapy in patients with dyslipidemia: results of Prysme study

Despite the availability of specific guidelines, the management of dyslipidemia in practice is not optimal.Objective and methodologyPRYSME, a non-interventional multicentre study carried out with 1226 general practitioners, aimed to describe the implementation time of a lipid lowering treatment according to cardiovascular risk level (primary objective) and to identify its determinants. Were eligible patients treated for a dyslipidemia diagnosed less than 2 years ago. Demographic and clinical characteristics and circumstances of diagnosis and treatment initiation were collected.Results3268 patients were included (mean age: 57 years old, males: 64%). 26% were obese and 45% overweight. Only 12% had no cardiovascular risk factors (CRF) at the time of dyslipidemia diagnosis. The most frequent CRF were arterial hypertension (50%), smoking (43%), family history of premature coronary heart disease (28%), HDL-c <0.4g/l (20%) whereas 15% of the patients had a personal history of cardiovascular disease. Dietary programs were initially implemented for 98% of the patients. More than 90% were treated with a statin. The implementation time of the treatment (evaluated according to the biological confirmation of dyslipidemia), according to the initial number of CRF, was as following:0 CRF1 CRF2 CRF≥ 3 CRFSecondary preventionTotal[-3;0] months34.3%28.6%27.1%29.3%49.1%33.1%]0;3] months23.1%26.2%26.4%24.0%21.9%23.9%> 3 months42.6%45.3%46.5%46.8%29.0%43.0%Chi-2 test : P<0.001The main determinant of an early implementation of a lipid lowering therapy (≤ 3 months) was secondary prevention (OR=1.8). The number of CRF had no significant impact.ConclusionThis study underlines the lack of awareness towards cardiovascular risk factors in the management of dyslipidemia, particularly while considering the implementation time of a lipid lowering therapy

Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

This article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.info:eu-repo/semantics/publishedVersio

Global robot Path Planning using GA for Large Grid Maps: Modelling, Performance and Experimentation

In this paper, the efficiency of genetic algorithm (GA) approach to address the problem of global path planning for mobile robots in large-scale grid environments is revisited and assessed. First, an efficient GA path planner to find an (or near) optimal path in a grid map is proposed. In particular, large maps instances are considered in this work, as small maps are easy to address by typical linear-time exact algorithms, in contrast to large maps, which require more intensive computations. The operators of the GA planner were carefully designed for optimizing the search process. Also, extensive simulations to evaluate the GA planner are conducted, and its performance is compared to that of the A algorithm considered as benchmarking reference. We found out that the GA planner can find optimal solutions in the same way as A in large grid maps in most cases, but A is faster than the GA. This is because GA is not a constructive path planner and heavily relies on initial population to explore the space of solutions in contrast to A that builds its solution progressively towards the target. It was concluded that, although GA can provide an alternative to A technique, it is likely that they are not efficient enough to beat exact methods such as A when used with a consistent heuristic. The GA planner is integrated in the global path planning modules of the Robot Operating System (ROS), its feasibility is demonstrated, and its performance is compared against the default ROS planner.info:eu-repo/semantics/publishedVersio