Objectives: The purpose of this study was to determine if implantation of multiple recalled defibrillator leads is associated with an increased risk of lead failure.
Background: The authors of the Pacemaker and Implantable Defibrillator Leads Survival Study ("PAIDLESS") have previously reported a relationship between recalled lead status, lead failure, and patient mortality. This substudy analyzes the relationship in a smaller subset of patients who received more than one recalled lead. The specific effects of having one or more recalled leads have not been previously examined.
Methods: This study analyzed lead failure and mortality of 3802 patients in PAIDLESS and compared outcomes with respect to the number of recalled leads received. PAIDLESS includes all patients at Winthrop University Hospital who underwent defibrillator lead implantation between February 1, 1996 and December 31, 2011. Patients with no recalled ICD leads, one recalled ICD lead, and two recalled ICD leads were compared using the Kaplan-Meier method and log-rank test. Sidak adjustment method was used to correct for multiple comparisons. All calculations were performed using SAS 9.4. P-values <.05 were considered statistically significant.
Results: This study included 4078 total ICD leads implanted during the trial period. There were 2400 leads (59%) in the no recalled leads category, 1620 leads (40%) in the one recalled lead category, and 58 leads (1%) in the two recalled leads category. No patient received more than two recalled leads. Of the leads categorized in the two recalled leads group, 12 experienced lead failures (21%), which was significantly higher (P<.001) than in the no recalled leads group (60 failures, 2.5%) and one recalled lead group (81 failures; 5%). Multivariable Cox's regression analysis found a total of six significant predictive variables for lead failure including the number of recalled leads (P<.001 for one and two recalled leads group).
Conclusions: The number of recalled leads is highly predictive of lead failure. Lead-based multivariable Cox's regression analysis produced a total of six predictive variable categories for lead failure, one of which was the number of recalled leads. Kaplan-Meier analysis showed that the leads in the two recalled leads category failed faster than both the no recalled lead and one recalled lead groups. The greater the number of recalled leads to which patients are exposed, the greater the risk of lead failure.
Download full-text PDF |
Source |
---|
Comput Biol Med
January 2025
Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan. Electronic address:
Background And Objective: Diabetic Retinopathy (DR) is a serious diabetes complication that can cause blindness if not diagnosed in its early stages. Manual diagnosis by ophthalmologists is labor-intensive and time-consuming, particularly in overburdened healthcare systems. This highlights the need for automated, accurate, and personalized machine learning approaches for early DR detection and treatment.
View Article and Find Full Text PDFDiscov Ment Health
January 2025
Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
This study aimed to present a complete overview of the trends, difficulties, and improvements in dental treatment for children diagnosed with autism spectrum disorder through rigorous bibliometric analysis. The dimensional database field was chosen to enable the inclusion and recall of the greatest number of relevant entries. All peer-reviewed international journals published between 2004 and 2023 were included in this study.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
School of Control Science and Engineering, Shandong University, Jinan, 250012, Shandong, China.
Early detection of colorectal cancer is vital for enhancing cure rates and alleviating treatment burdens. Nevertheless, the high demand for screenings coupled with a limited number of endoscopists underscores the necessity for advanced deep learning techniques to improve screening efficiency and accuracy. This study presents an innovative convolutional neural network (CNN) model, trained on 8260 images from screenings conducted at four medical institutions.
View Article and Find Full Text PDFSubcell Biochem
January 2025
Faculty of Medicine and Faculty of Life Sciences, Institute of Biomedical Sciences (ICB), Universidad Andres Bello, Santiago, Chile.
In animals, memory formation and recall are essential for their survival and for adaptations to a complex and often dynamically changing environment. During memory formation, experiences prompt the activation of a selected and sparse population of cells (engram cells) that undergo persistent physical and/or chemical changes allowing long-term memory formation, which can last for decades. Over the past few decades, important progress has been made on elucidating signaling mechanisms by which synaptic transmission leads to the induction of activity-dependent gene regulation programs during the different phases of learning (acquisition, consolidation, and recall).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!