Background: Infections of cardiac implantable devices (CIEDI) have poor outcomes despite improvement in lead extraction (TLE) procedures. Methods: To explore the influence of CIEDI on the outcomes of TLE and the differences between patients with systemic (Sy) vs. local (Lo) CIEDI, we performed a sub-analysis of the EORP ELECTRa (European Lead Extraction ConTRolled) Registry. Results: Among 3555 patients enrolled by 73 centers in 19 Countries, the indication for TLE was CIEDI in 1850: 1170 with Lo-CIEDI and 680 with Sy-CIEDI. Patients with CIEDI had a worse in-hospital prognosis in terms of major complications (3.57% vs. 1.71%; p = 0.0007) and mortality (2.27% vs. 0.49%; p < 0.0001). Sy-CIEDI was an independent predictor of in-hospital death (H.R. 2.14; 95%CI 1.06−4.33. p = 0.0345). Patients with Sy-CIEDI more frequently had an initial CIED implant and a higher prevalence of comorbidities, while subjects with Lo-CIEDI had a higher prevalence of previous CIED procedures. Time from signs of CIEDI and TLE was longer for Lo-CIEDI despite a shorter pre-TLE antibiotic treatment. Conclusions: Patients with CIEDI have a worse in-hospital prognosis after TLE, especially for patients with Sy-CIEDI. These results raise the suspicion that in a relevant group of patients CIEDI can be systemic from the beginning without progression from Lo-CIEDI. Future research is needed to characterize this subgroup of patients.
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http://dx.doi.org/10.3390/biology11040615 | DOI Listing |
Sci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
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View Article and Find Full Text PDFJ Environ Manage
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School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
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View Article and Find Full Text PDFNat Commun
January 2025
School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, China.
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January 2025
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View Article and Find Full Text PDFComput Biol Chem
December 2024
School of Software, Henan Polytechnic University, Jiaozuo 454003, China. Electronic address:
Background: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown potential to reduce costs and enhance discovery efficiency by improving protein target identification accuracy. Additionally, with an urgent need for novel therapies against complex diseases, CPI investigation could lead to the identification of effective new drugs.
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