Objectives: This study sought to examine the safety and efficacy of laser-assisted lead extraction and the indications, outcomes, and risk factors in a large series of consecutive patients.
Background: The need for lead extraction has been increasing in direct relationship to the increased numbers of cardiovascular implantable electronic devices.
Methods: Consecutive patients undergoing transvenous laser-assisted lead extraction at 13 centers were included.
Results: Between January 2004 and December 2007, 1,449 consecutive patients underwent laser-assisted lead extraction of 2,405 leads (20 to 270 procedures/site). Median implantation duration was 82.1 months (0.4 to 356.8 months). Leads were completely removed 96.5% of the time, with a 97.7% clinical success rate whereby clinical goals associated with the indication for lead removal were achieved. Failure to achieve clinical success was associated with body mass index <25 kg/m(2) and low extraction volume centers. Procedural failure was higher in leads implanted for >10 years and when performed in low volume centers. Major adverse events in 20 patients were directly related to the procedure (1.4%) including 4 deaths (0.28%). Major adverse effects were associated with patients with a body mass index <25 kg/m(2). Overall all-cause in-hospital mortality was 1.86%; 4.3% when associated with endocarditis, 7.9% when associated with endocarditis and diabetes, and 12.4% when associated with endocarditis and creatinine > or =2.0. Indicators of all-cause in-hospital mortality were pocket infections, device-related endocarditis, diabetes, and creatinine > or =2.0.
Conclusions: Lead extraction employing laser sheaths is highly successful with a low procedural complication rate. Total mortality is substantially increased with pocket infections or device-related endocarditis, particularly in the setting of diabetes, renal insufficiency, or body mass index <25 kg/m(2). Centers with smaller case volumes tended to have a lower rate of successful extraction.
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http://dx.doi.org/10.1016/j.jacc.2009.08.070 | DOI Listing |
J Am Coll Cardiol
December 2024
Electrophysiology Section, Division of Cardiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sensors (Basel)
December 2024
School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK.
The rapid integration of Internet of Things (IoT) systems in various sectors has escalated security risks due to sophisticated multilayer attacks that compromise multiple security layers and lead to significant data loss, personal information theft, financial losses etc. Existing research on multilayer IoT attacks exhibits gaps in real-world applicability, due to reliance on outdated datasets with a limited focus on adaptive, dynamic approaches to address multilayer vulnerabilities. Additionally, the complete reliance on automated processes without integrating human expertise in feature selection and weighting processes may affect the reliability of detection models.
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December 2024
Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process.
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December 2024
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep's impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming.
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December 2024
Bioprospecting Research Group, School of Engineering, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía 140013, Colombia.
Fresh-cutting fruits is a common practice in markets and households, but their short shelf life is a challenge. Active packaging is a prominent strategy for extending food shelf life. A systematic review was conducted following the PRISMA guidelines to explore the performance and materials used in biodegradable active packaging for fresh-cut fruits.
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