Background: Cardiac resynchronization therapy (CRT) improves symptoms, left ventricular ejection fraction (LVEF) and survival in patients with heart failure and wide QRS, however, long term clinical outcome is unknown.
Aims: To identify predictors of mortality and evaluate the effects of CRT after long term follow-up.
Methods: Consecutive patients treated with CRT between 1997 and 2002 were included. We collected clinical information from patient files. Patients who were still alive underwent echocardiography and clinical evaluation.
Results: We included 179 patients (median age 65.5 years, 144 male). Median follow-up for survival was 4.0 years. Mortality at one and five years was 15% and 53%, respectively. Predictors of mortality were, ischaemic heart disease (IHD), higher NYHA class and lower LVEF (<22.5%) at baseline, and no improvement in NYHA class at early follow-up. NYHA class remained stable from early to long term follow-up after a median of 5.1 years. In patients with non-IHD median LVEF increased significantly from early to long term follow-up (39% vs. 50% p=0.007).
Conclusion: Predictors of mortality in patients with CRT are IHD, lower LVEF and higher NYHA class at baseline, and no symptomatic response to CRT. After 5 years follow-up, clinical effects are sustained, and in patients with non-IHD further improvements in LVEF are observed.
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http://dx.doi.org/10.1016/j.ejheart.2008.06.013 | DOI Listing |
Sci Rep
December 2024
Clermont Auvergne University, CNRS, IRD, OPGC, Magmas and Volcanoes Laboratory, 63000, Clermont-Ferrand, France.
The new submarine volcano Fani Maoré offshore Mayotte (Comoros archipelago) discovered in 2019 has raised the awareness of a possible future eruption in Petite-Terre island, located on the same 60 km-long volcanic chain. In this context of a renewal of the volcanic activity, we present here the first volcanic hazard assessment in Mayotte, focusing on the potential reactivation of the Petite-Terre eruptive centers. Using the 2-D tephra dispersal model HAZMAP and the 1979 - 2021 meteorological ERA-5 database, we first identify single eruptive scenarios of various impacts for the population of Mayotte.
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December 2024
Department of Urology, The Jikei University School of Medicine, Kashiwa Hospital, Kashiwashita 163-1, Kashiwa, Chiba, 277-8567, Japan.
To evaluate the safety and efficacy of the Saroa Surgical Robot System in robot-assisted laparoscopic radical prostatectomy (RARP). We enrolled 60 patients who underwent RARP using either the Saroa (n = 9) or da Vinci Xi (n = 51) systems at Jikei University Kashiwa Hospital from January 2022 to March 2024. We compared preoperative characteristics, perioperative outcomes, complications, and postoperative urinary continence at three months between the two groups.
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December 2024
Jihua Laboratory, Foshan, 528000, China.
Surface-enhanced Raman scattering (SERS) technology has attracted more and more attention due to its high sensitivity, low water interference, and quick measurement. Constructing high-performance SERS substrates with high sensitivity, uniformity and reproducibility is of great importance to put the SERS technology into practical application. In this paper, we report a simple fabrication process to construct dense silver-coated PMMA nanoparticles-on-a-mirror SRES substrates.
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December 2024
Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran.
This study investigates the potential impacts of climate change on the distribution of Iranian amphibian species and identifies refugia and biodiversity hotspots to inform effective conservation strategies. The study employed ensemble species distribution models to assess the impacts of climate change on 19 Iranian amphibian species. We analyzed future scenarios (2041-2060 & 2081-2100) under a high-emission pathway to identify potential range shifts and refugia (areas with stable or newly suitable climate).
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December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Kyiv, Ukraine.
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.
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