Heart : official journal of the British Cardiac Society
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Observational Study
Outcomes after sudden cardiac arrest in sports centres with and without on-site external defibrillators.
Sudden cardiac arrest (SCA) is a rare but tragic event during amateur sports activities. Our aim is to analyse whether availability of automated external defibrillators (AEDs) in amateur sports centres could impact on SCA survival. ⋯ The presence of on-site AEDs is associated with neurologically intact survival after an exercise-related SCA. Continuous efforts are recommended in order to introduce AEDs in sports and fitness centres, implement educational programmes and increase common awareness about SCA.
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Atrial fibrillation (AF) in hypertrophic cardiomyopathy (HCM) is associated with increased mortality, mainly mediated by increased thromboembolic events and progressive heart failure. Many studies suggested inhibition of renin-angiotensin-aldosterone system (RAAS) could reduce new AF in various clinical conditions. However, evidence concerning the effects of RAAS inhibitors on AF prevention remains unclear in HCM. Our study is to investigate whether treatment with ACE inhibitors (ACEIs) or angiotensin-receptor blockers (ARBs) could lower the risk of new AF in HCM. ⋯ In patients with HCM, lower risk of new AF is observed in patients treated with either ACEIs or ARBs compared with those receiving neither of these medications.
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Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. ⋯ The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.
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Meta Analysis
Genome-wide association study of PR interval in Hispanics/Latinos identifies novel locus at ID2.
PR interval (PR) is a heritable electrocardiographic measure of atrial and atrioventricular nodal conduction. Changes in PR duration may be associated with atrial fibrillation, heart failure and all-cause mortality. Hispanic/Latino populations have high burdens of cardiovascular morbidity and mortality, are highly admixed and represent exceptional opportunities for novel locus identification. However, they remain chronically understudied. We present the first genome-wide association study (GWAS) of PR in 14 756 participants of Hispanic/Latino ancestry from three studies. ⋯ Our results suggest that genetic determinants of PR are consistent across race/ethnicity, but extending studies to admixed populations can identify novel associations, underscoring the importance of conducting genetic studies in diverse populations.