Introduction: Cardiac arrhythmias, including ventricular tachycardia (VT), stand as a significant threat to health, often leading to mortality and sudden cardiac death. While conventional treatments for VT exhibit efficacy, cases of refractory VT pose challenges. Stereotactic Arrhythmia Radioablation (STAR) offers a novel approach, delivering precise high-dose radiation to well-defined targets with minimal collateral damage. This study explores the potential of STAR as an alternative therapy, especially for high-risk patients or those with refractory VT.
Methods: This research reviews ongoing studies and preliminary investigations into the evaluation of the efficacy and safety of STAR. The method involves targeted radiation delivery, assessing reductions in VT recurrence and the early safety profile in refractory VT patients. However, given STAR's early stage and limited clinical evidence, cautious interpretation is advised.
Results: Preliminary findings indicate a reduction in VT recurrence with STAR, suggesting promise as a therapeutic option. Early safety profiles are encouraging, but definitive statements on efficacy and safety require further investigation. Positive initial outcomes underscore the need for additional data and long-term studies.
Conclusion: Stereotactic Arrhythmia Radioablation is recently emerging as a promising treatment for refractory VT. While early results are encouraging, careful interpretation is needed, due to STAR's early stages. Ongoing investigations are critical for a comprehensive understanding of its long-term efficacy and tolerability. This review provides fundamental insights into STAR's background, principles, pre-treatment procedures, clinical implications, and toxicity, setting the stage for future research in this evolving therapeutic field.
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http://dx.doi.org/10.1002/hsr2.1741 | DOI Listing |
Sensors (Basel)
December 2024
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar.
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Department of Pneumology, Oncology and Allergology, Medical University of Lublin, Jaczewskiego 8, 20-090 Lublin, Poland.
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Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), University of Utah School of Medicine, 95 S 2000 E, Salt Lake City, UT 84112, USA.
Heart failure with preserved ejection fraction (HFpEF) is increasing at an alarming rate worldwide, with limited effective therapeutic interventions in patients. Sudden cardiac death (SCD) and ventricular arrhythmias present substantial risks for the prognosis of these patients. Obesity is a risk factor for HFpEF and life-threatening arrhythmias.
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Department of Medical Biology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands.
Short QT syndrome type 3 (SQTS3 or SQT3), which is associated with life-threatening cardiac arrhythmias, is caused by heterozygous gain-of-function mutations in the gene. This gene encodes the pore-forming α-subunit of the ion channel that carries the cardiac inward rectifier potassium current (I). These gain-of-function mutations either increase the amplitude of I or attenuate its rectification.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
Department of Physiology II, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Ishikawa 920-0293, Japan.
Arrhythmias are divided into supraventricular and ventricular, depending on where they originate [...
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