Background: The hybrid algorithm for chronic total occlusion (CTO) percutaneous coronary intervention (PCI) was developed to improve procedural outcomes. Large, prospective studies validating the algorithm in a broad multicenter setting with operators of different experience levels are lacking.
Objectives: The RECHARGE (REgistry of Crossboss and Hybrid procedures in FrAnce, the NetheRlands, BelGium and UnitEd Kingdom) registry aims to report achievable results using the hybrid algorithm.
Methods: Between January 2014 and October 2015, consecutive patients undergoing hybrid CTO-PCI were prospectively enrolled in 17 centers. Procedural techniques, outcomes, and in-hospital complications were analyzed.
Results: A total of 1,253 CTO-PCIs were performed in 1,177 patients, of which 86% were men. Mean age was 66 ± 11 years. The average Japanese CTO score was 2.0 ± 1.0, and was higher in the failure group (2.6 ± 0.6 vs. 1.9 ± 1.0; p < 0.001). Overall procedure success was 86% and major in-hospital complications occurred in 2.6%. Antegrade wire escalation was the preferred primary strategy in 77%, followed by retrograde (17%) and antegrade dissection re-entry strategies (7%). Primary strategies were successful in 60%. Consecutive strategies were applied in 34% and were successful in 74%. Antegrade dissection re-entry and retrograde strategies were the most common bailout strategies and were successful in 67% and 62%, respectively. Median procedure and fluoroscopy time were 90 (interquartile range [IQR]: 60 to 120) min and 35 (IQR: 21 to 55) min, contrast volume was 250 (IQR: 180 to 340) ml, and radiation doses (air kerma and dose area product) were 1.6 (IQR: 1.0 to 2.7) Gy and 98 (IQR: 57 to 168) Gy·cm, respectively.
Conclusions: High procedure and patient success rates, combined with a low event rate and improved procedural characteristics, support further use of the hybrid algorithm for a broad community of appropriately trained CTO operators.
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http://dx.doi.org/10.1016/j.jacc.2016.08.034 | DOI Listing |
Sci Rep
January 2025
School of Electronics and Information, Xijing University, Xi'an, 710123, China.
To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery.
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January 2025
Faculty of Engineering, Helwan University, Cairo, Egypt.
Frequency regulation in isolated microgrids is challenging due to system uncertainties and varying load demands. This study presents an optimal µ-synthesis robust control strategy that regulates microgrid frequency while enhancing system performance and stability-a proposed fixed-structure approach for selecting performance and robustness weights, informed by subsystem frequency analysis. The controller is optimized using multi-objective particle swarm optimization (MOPSO) and multi-objective genetic algorithm (MOGA) under inequality constraints, employing a Pareto front to identify optimal solutions.
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January 2025
Business School, Sichuan University, 610059, Chengdu, China.
The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making.
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January 2025
Faculty of Computers and Information, Minia University, Minia, Egypt.
This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency's Downscaler Model (DS) to predict Particulate Matter ([Formula: see text]) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time.
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