Purpose: To report on the clinical utility of implantable loop recorders (ILRs) in a large academic hospital setting over a 4-year period.
Methods: Retrospective study (2013-2016) of patients receiving ILRs for any indication including syncope, cryptogenic stroke (CrS), atrial fibrillation (AF) burden, palpitations, ventricular arrhythmias (VA), and other. Remote checks, symptomatic transmissions, and in-person checks were reviewed. Time to diagnosis was documented.
Results: A total of 263 patients (54% male, mean age 63 ± 15 years, mean follow-up 601 (range 9-1714) days) received ILRs for 324 indications; multiple indications were noted in 53/263 (20.2%) patients. ILR indications were 126 (39%) syncope, 81 (25%) CrS, 46 (14%) AF, 37 (11%) palpitations, 10 (3%) VA, and 24 (7%) other. Diagnostic yield for each indication was compared to the overall yield for all other indications. Three indications showed a significantly higher yield: AF (65% vs. 22%, p < 0.002), palpitations (60% vs. 24%, p < 0.001), and VA (70% vs. 28%, p < 0.004). For all other indications, there were no significant differences. Syncope had nearly half the diagnostic yield of previously published trials (28% vs. 43-56%). We observed a fourfold increase in ILR implant rate over the study duration.
Conclusions: In a "real-world" academic hospital setting, the diagnostic rate of ILRs was highest for AF, palpitations, and VA; however, these high yield indications comprised only 29% of all indications. The diagnostic yield for the commonest indication (syncope) was approximately half that reported in the previously published trials. With increasing implantation rates, additional studies are required to refine guideline-based indications for ILR implantation to improve diagnostic yield.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s10840-020-00815-w | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.
JMIR Cancer
January 2025
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging.
View Article and Find Full Text PDFComparison of the diagnostic performance of non-contrast MR angiography and planar V/Q scintigraphy for pulmonary embolism: a systematic review and meta-analysis.
Eur Radiol
January 2025
Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil.
Objectives: To conduct a meta-analysis of the diagnostic performance of non-contrast magnetic resonance pulmonary angiography (NC-MRPA) and ventilation-perfusion (V/Q) scintigraphy for the detection of acute pulmonary embolism (PE).
Materials And Methods: Systematic searches of electronic databases were conducted from 2000 to 2024. Primary outcomes were per-patient sensitivity and specificity of NC-MRPA and V/Q scintigraphy.
To establish a multivariate linear regression model for predicting the difficulty of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids based on multi-sequence magnetic resonance imaging radiomics features. A retrospective analysis was conducted on 218 patients with uterine fibroids who underwent HIFU treatment, including 178 cases from Yongchuan Hospital of Chongqing Medical University and 40 cases from the Second Affiliated Hospital of Chongqing Medical University (external validation set). Radiomics features were extracted and selected from magnetic resonance images, and potentially related imaging features were collected.
View Article and Find Full Text PDFColorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach.
Sci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFEvaluation of an indirect Na1.7 inhibitor as adjunctive analgesic in burn-related neuropathic pain in a cat.
Vet Anaesth Analg
January 2025
Department of Pharmacology and Therapeutics, University of Florida, College of Medicine, Gainesville, FL, USA.
Burn-related neuropathic pain (BRNP) can arise following burn-induced nerve damage, affects approximately 6% of burned human patients and can result in chronic pain. Although widely studied in humans, data on BRNP or its treatment in animals is lacking. A 4-year-old domestic shorthair cat was presented with an infected, non-healing wound suspected to be a caustic burn.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!