Publications by authors named "G R Wynn"
Oldest-in-Human Successful Extraction Experience of a Novel Substernal Extravascular Defibrillator.
J Cardiovasc Electrophysiol
January 2025
Introduction: Substernal extravascular defibrillators (EV ICDs) have been shown to be effective and safe for patients at risk of sudden cardiac death, however, there is little evidence around the safety of extracting chronic devices.
Methods And Results: We present a 50-year-old patient in whom a Medtronic EV ICD system was successfully removed without specialist extraction tools, 186 weeks after implantation, by an operator experienced in transvenous lead extraction but without formal training in EVICD implantation.
Conclusion: The successful extraction of an EV ICD system is possible without specialised tools at least 3.
A Case of Very Early Lead Fracture in an Implantable Cardioverter-Defibrillator: Management and Follow-Up.
Pacing Clin Electrophysiol
January 2025
Article Synopsis
- Implantable cardioverter defibrillator (ICD) lead fractures are rare but can cause inappropriate shocks, and early occurrences post-implant are very unusual.
- A 53-year-old male with hypertrophic cardiomyopathy received an inappropriate shock just 4 days after ICD implantation due to a lead fracture, which was successfully extracted and replaced without immediate complications.
- Early lead fractures highlight the need for clinicians to be vigilant, and remote monitoring can help detect these issues promptly.
Bringing big data to vascular complications during atrial fibrillation ablation.
J Cardiovasc Electrophysiol
August 2024
Importance: Posttraumatic stress disorder (PTSD) is a prevalent mental health problem that increases risk of cardiovascular disease (CVD). It is not known whether gender or comorbidities modify associations between PTSD and CVD.
Objective: To assess risk of hypertension and atherosclerotic CVD (ASCVD) associated with PTSD in a predominantly young military population, and determine if gender or PTSD comorbidities modify these associations.
Article Synopsis
- The study focuses on improving treatment for military personnel suffering from insomnia by using a machine learning model to predict responses to cognitive behavioral therapy for insomnia (CBT-I).
- The model analyzed data from 1,449 US Army soldiers and achieved a prediction accuracy of about 60%, identifying significant predictors like insomnia severity and suicidal ideation.
- The results suggest that using this model could help target individuals who are more likely to benefit from CBT-I, promoting a personalized approach to insomnia treatment in military settings.