Aims: The aim of this prospective multicentre study is to evaluate safety and efficacy of the new bidirectional rotational mechanical lead extraction (LE) sheath (Evolution RL, Cook Medical, USA) in chronically implanted leads (>1-year-old leads).
Methods And Results: Between September 2013 and June 2016, a total of 238 leads in 124 consecutive patients were removed by using the new Evolution RL rotational mechanical sheath. Indications for LE were cardiac device infection in 63 (50.8%) cases, lead malfunction in 41 (33.1%), upgrade in 1 (0.8%) case and for other reasons in the remaining 19 cases (15.3%). Ninety-one leads (38.2%) were implantable cardioverter defibrillator leads (81 dual coil vs. 10 single coil), 38 (16%) right ventricular leads, 86 (36.1%) right atrial leads, and 23 (9.7%) coronary sinus leads. The mean implant duration was 92.2 ± 52.9 months (range 12-336). 91.6% of the leads (218/238) were extracted completely with the Evolution RL alone, with the complete success rate rising to 98.7% (235/238 leads) with combined use of a snare. Overall clinical success rate was 100%. No Evolution sheath-related complications were noted. There were no deaths or major complications. Five minor complications (4%) were encountered. In cases of companion leads no wrapping or lead damage were observed.
Conclusion: On the basis of our prospective multicentre study, the new hand-powered bidirectional rotational mechanical LE sheath is an effective and safe tool for the extraction of chronically implanted leads without major complications and lead wrapping or lead damage.
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http://dx.doi.org/10.1093/europace/eux020 | DOI Listing |
We propose a low-polarization-sensitive 1 × 2 carrier-injection-type silicon photonic switch consisting of a single Mach-Zehnder interferometer, an input-/output-side polarization splitter and rotators, bidirectional light injection, and an external optical circulator. A polarization-dependent loss (PDL) of 1.3 dB was achieved using the proposed structure, whereas a PDL exceeding 17 dB was observed without the structure.
View Article and Find Full Text PDFPlant Dis
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The Ohio State University, Plant Pathology, 2021 Coffey Road, Columbus, Ohio, United States, 43210;
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Action segmentation is a challenging task in high-level process analysis, typically performed on video or kinematic data obtained from various sensors. This work presents two contributions related to action segmentation on kinematic data. Firstly, we introduce two versions of Multi-Stage Temporal Convolutional Recurrent Networks (MS-TCRNet), specifically designed for kinematic data.
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