Subcutaneous ICD (S-ICD™) therapy has been established in initial clinical trials and current international guideline recommendations for patients without demand for pacing, cardiac resynchronization, or antitachycardia pacing. The promising experience in ‘ideal’ S-ICD™ candidates increasingly encourages physicians to provide the benefits of S-ICD™ therapy to patients in clinical constellations beyond ‘classical’ indications of S-ICD™ therapy, which has led to a broadening of S-ICD™ indications in many centres. However, the decision for S-ICD™ implantation is still not covered by controlled randomized trials but rather relies on patient series or observational studies. Thus, this review intends to give a contemporary update on available empirical evidence data and technical advancements of S-ICD™ technology and sheds a spotlight on S-ICD™ therapy in recently discovered fields of indication beyond ideal preconditions. We discuss the eligibility for S-ICD™ therapy in Brugada syndrome as an example for an adverse and dynamic electrocardiographic pattern that challenges the S-ICD™ sensing and detection algorithms. Besides, the S-ICD™ performance and defibrillation efficacy in conditions of adverse structural remodelling as exemplified for hypertrophic cardiomyopathy is discussed. In addition, we review recent data on potential device interactions between S-ICD™ systems and other implantable cardio-active systems (e.g. pacemakers) including specific recommendations, how these could be prevented. Finally, we evaluate limitations of S-ICD™ therapy in adverse patient constitutions, like distinct obesity, and present contemporary strategies to assure proper S-ICD™ performance in these patients. Overall, the S-ICD™ performance is promising even for many patients, who may not be ‘classical’ candidates for this technology.
Introduction: Automated echocardiography image interpretation has the potential to transform clinical practice. However, neural networks developed in general cohorts may underperform in the setting of altered cardiac anatomy. Methods: Consecutive echocardiographic studies of patients with congenital or structural heart disease (C/SHD) were used to validate an existing convolutional neural network trained on 14,035 echocardiograms for automated view classification. In addition, a new convolutional neural network for view classification was trained and tested specifically in patients with C/SHD. Results: Overall, 9793 imaging files from 262 patients with C/SHD (mean age 49 years, 60% male) and 62 normal controls (mean age 45 years, 50.0% male) were included. Congenital diagnoses included among others, tetralogy of Fallot (30), Ebstein anomaly (18) and transposition of the great arteries (TGA, 48). Assessing correct view classification based on 284,250 individual frames revealed that the non-congenital model had an overall accuracy of 48.3% for correct view classification in patients with C/SHD compared to 66.7% in patients without cardiac disease. Our newly trained convolutional network for echocardiographic view detection based on over 139,910 frames and tested on 35,614 frames from C/SHD patients achieved an accuracy of 76.1% in detecting the correct echocardiographic view. Conclusions: The current study is the first to validate view classification by neural networks in C/SHD patients. While generic models have acceptable accuracy in general cardiology patients, the quality of image classification is only modest in patients with C/SHD. In contrast, our model trained in C/SHD achieved a considerably increased accuracy in this particular cohort.
For the first time, valid data of Achilles tendon diameters in competitive athletes and normal individuals have been presented. The emerging pattern of results clearly contradicts the notion of a physiological training adaptation of the Achilles tendon.
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