Aims: The subcutaneous implantable cardioverter defibrillator (S-ICD) was introduced to overcome complications related to transvenous leads. Adoption of the S-ICD requires implanters to learn a new implantation technique. The aim of this study was to assess the learning curve for S-ICD implanters with respect to implant-related complications, procedure time, and inappropriate shocks (IASs).
Methods And Results: In a pooled cohort from two clinical S-ICD databases, the IDE Trial and the EFFORTLESS Registry, complications, IASs at 180 days follow-up and implant procedure duration were assessed. Patients were grouped in quartiles based on experience of the implanter and Kaplan-Meier estimates of complication and IAS rates were calculated. A total of 882 patients implanted in 61 centres by 107 implanters with a median of 4 implants (IQR 1,8) were analysed. There were a total of 59 patients with complications and 48 patients with IAS. The complication rate decreased significantly from 9.8% in Quartile 1 (least experience) to 5.4% in Quartile 4 (most experience) (P = 0.02) and non-significantly for IAS from 7.9 to 4.8% (P = 0.10). Multivariable analysis demonstrated a hazard ratio of 0.78 (P = 0.045) for complications and 1.01 (P = 0.958) for IAS. Dual-zone programming increased with experience of the individual implanter (P < 0.001), which reduced IAS significantly in the multivariable model (HR 0.44, P = 0.01). Procedure time decreased from 75 to 65 min (P < 0.001). The complication rate and procedure time stabilized after Quartile 2 (>13 implants).
Conclusion: There is a short and significant learning curve associated with physicians adopting the S-ICD. Performance stabilizes after 13 implants.
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http://dx.doi.org/10.1093/europace/euv299 | DOI Listing |
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFJ Comput Assist Tomogr
November 2024
From the Department of Medical Imaging, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin City, Jiangsu Province, China.
Objectives: The aims of the study are to predict lung function impairment in patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD) through computed tomography (CT) quantitative analysis parameters based on CT deep learning model and density threshold method and to assess the severity of the disease in patients with CTD-ILD.
Methods: We retrospectively collected chest high-resolution CT images and pulmonary function test results from 105 patients with CTD-ILD between January 2021 and December 2023 (patients staged according to the gender-age-physiology [GAP] system), including 46 males and 59 females, with a median age of 64 years. Additionally, we selected 80 healthy controls (HCs) with matched sex and age, who showed no abnormalities in their chest high-resolution CT.
PLoS Genet
January 2025
Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, Ontario, Canada.
Innovative and easy-to-implement strategies are needed to improve the pathogenicity assessment of rare germline missense variants. Somatic cancer driver mutations identified through large-scale tumor sequencing studies often impact genes that are also associated with rare Mendelian disorders. The use of cancer mutation data to aid in the interpretation of germline missense variants, regardless of whether the gene is associated with a hereditary cancer predisposition syndrome or a non-cancer-related developmental disorder, has not been systematically assessed.
View Article and Find Full Text PDFRadiol Phys Technol
January 2025
Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.
Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures.
View Article and Find Full Text PDFTransl Stroke Res
January 2025
Department of Neurosurgery, Shanxi Provincial People's Hospital, Taiyuan, Shanxi, China.
Spontaneous intracranial artery dissection (sIAD) is the leading cause of stroke in young individuals. Identifying high-risk sIAD cases that exhibit symptoms and are likely to progress is crucial for treatment decision-making. This study aimed to develop a model relying on circulating biomarkers to discriminate symptomatic sIADs.
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