News Update on Atrial Fibrillation Research: May – 2019
A deep learning approach for real-time detection of atrial fibrillation
Goal: To develop a strong and period approach for automatic detection of arrhythmia (AF) in long graph (ECG) recordings victimisation deep learning (DL). Method: AN end-to-end model combining the Convolutional- and Recurrent-Neural Networks (CNN and RNN) was projected to extract high level options from segments of RR intervals (RRIs) so as to classify them as AF or traditional sinus rhythm (NSR). Results: The model was trained and valid on 3 completely different databases together with a complete of eighty nine subjects. It achieved a sensitivity and specificity of ninety eight.98% and 96.95%, severally, valid through a 5-fold cross-validation. in addition, the projected model was found to be computationally economical and it had been capable of analyzing twenty four h of cardiogram recordings in but one second. The projected rule was conjointly tested on the unseen datasets to look at its lustiness in detective work AF for brand new recordings that resulted in ninety eight.96% and 86.04% for specificity and sensitivity, severally. Conclusion: Compared to the progressive models evaluated on commonplace benchmark cardiogram datasets, the projected model made higher performance in detective work AF. in addition, since the model learns options directly from the information, it avoids the requirement for clever/cumbersome feature engineering. 
Cardioversion of atrial fibrillation in obese patients: Results from the Cardioversion‐BMI randomized controlled trial
Obesity is related to higher electrical cardioversion (ECV) failure in persistent arrhythmia (PeAF). For ease‐of‐use, several centers like patches over paddles. we have a tendency to assessed the optimum modality and shock vector, furthermore because the safety and effectiveness of the Manual Pressure Augmentation (MPA) technique.
Patients with fat (BMI ≥ 30) and PeAF undergoing ECV employing a biphasic electronic device were randomised into one amongst four arms by modality (adhesive patches or hand-held paddles) and shock vector (anteroposterior [AP] or anteroapical [AA]). If the primary 2 shocks (100 and 200 J) unsuccessful, then patients received a 200‐J shock victimisation the choice modality (patch or paddle). Shock vector remained unchanged. In associate degree empiric substudy, twenty patients with BMI of thirty five or a lot of, and World Health Organization unsuccessful ECV at 200 J victimisation each patches/paddles underwent an attempt of MPA.
In total, a hundred twenty five patients were randomised between July 2016 and March 2018. 1st or second shock success was forty three of sixty three (68.2%) for patches and fifty six of sixty two (90.3%) for paddles (P = 0.002). there have been twenty crossovers from patches to paddles (12 of 20 third shock success with paddles) and 6 crossovers from paddles to patches (three of six third shock success with patches). Paddles with success cardioverted sixty eight of eighty two patients compared with forty six of sixty nine victimisation patches (82.9% vs 66.7%; P = 0.02). Shock vector failed to influence 1st or second shock success rates (82.0% AP vs seventy six.6% AA; P = 0.46). MPA was sure-fire in sixteen of twenty (80%) World Health Organization unsuccessful in each (patches/paddles), with 360 J needed in six of seven cases.
Routine use of adhesive patches at 200 J is insufficient in fat. ways that improve success embrace the employment of paddles, MPA, and increase to 360 J.
Planning Telehealth for Older Adults With Atrial Fibrillation in Rural Communities: Understanding Stakeholder Perspectives
Older adults with cardiac arrhythmia (AF) in rural communities have less access to viscus specialty care. Telehealth offers a viable approach to supply viscus care, however very little is thought concerning patients’ and providers’ views on telehealth’s potential to support rural patients with AF. This qualitative descriptive study examines patient and health providers’ views, a crucial commencement in designing a telehealth initiative. Eight patients with AF, together with one partner from rural communities, were recruited through associate degree urban-based AF clinic. 5 suppliers were recruited through skilled follow leads within the health region. Semistructured phonephone interviews were conducted with each neutral teams. The paramount theme was variability in patient and supplier receptivity to telehealth. receptivity mirrored variations in past expertise with telehealth, in perceived adequacy of rural health services, and in perceived gaps in AF care. These are vital concerns in designing effective and property telehealth in rural communities.
Biobank-driven genomic discovery yields new insight into atrial fibrillation biology
To identify genetic variation underlying arrhythmia, the foremost common arrhythmia, we tend to performed a genome-wide association study of >1,000,000 people, as well as sixty,620 arrhythmia cases and 970,216 controls. we tend to known 142 freelance risk variants at 111 loci and prioritized 151 useful candidate genes probably to be concerned in arrhythmia. several of the known risk variants fall close to genes wherever additional harmful mutations are rumored to cause serious heart defects in humans (GATA4, MYH6, NKX2-5, PITX2, TBX5)1, or close to genes vital for muscle operate and integrity (for example, CFL2, MYH7, PKP2, RBM20, SGCG, SSPN). Pathway and useful enrichment analyses additionally advised that a lot of of the acknowledged arrhythmia genes act via internal organ structural transforming, doubtless within the type of associate ‘atrial cardiomyopathy’2, either throughout foetal heart development or as a response to fret within the adult heart. 
Dronedarone after Catheter Ablation of Atrial Fibrillation: A New Option in Hybrid Therapy
Background and Aim: tube ablation has become the medical aid of selection in patients with symptomatic, recurrent, drug-refractory fibrillation (AF). However, frequent AF recurrences typically necessitate AN connected medication (AAD) medical aid. Dronedarone could be a new category III AAD with modest aspect effects. we have a tendency to compared a traditional AAD medical aid (CAAT) with category I/III AADs to a completely unique therapy with dronedarone (NAAT)in relation to AF recurrences and improvement of symptoms.
Methodology: 100 twenty 5 consecutive patients (84 men; mean age sixty two.1±12.4 years) with symptomatic attack (n=70) or persistent (n=55) drug refractory AF were registered in AN open-label irregular study. Following prospering venous blood vessel isolation (PVI) patients were irregular to receive CAAT (n=50), NAAT (n=50) or no AAD medical aid (=control; n=25). Follow-up visits were regular at three, 6, 9, and twelve months post ablation. Seven-day-Holter watching and patients’ histories served as indicators of treatment success. Bar signs of AF repetition AADs were out of print half-dozen months post ablation.
Results: The pre-ablation European cardiac rhythm Association (EHRA)-score diminished from two.8±0.4 to 1.4±0.6 (NAAT) and one.5±0.7 (CAAT) half-dozen months when PVI (1.7±0.7 within the management group). Fifty patients practised an cardiac arrhythmia repetition inside three months. when half-dozen months, each hybrid medical aid teams showed a big advantage over the management cluster pro sinus rhythm (SR).Whereas CAAT might retain its significant profit at nine months NAAT lost its relative benefits with solely a positive trend remaining over the management cluster however a big disadvantage compared to CAAT patients. At this time AF recurrences were found in thirty four of NAAT patients, twenty six of CAAT patients, and fortieth of management patients. At twelve months, however, no cluster might preserve a big lead over either of the others.
Conclusion: Dronedarone when PVI is safe and effective. Compared to a CAAT, NAAT reveals similar enhancements of EHRA-scores and non-significantly totally different AF repetition rates from nine months on. Despite this, CAAT keeps considerably a lot of patients in SR nine months when PVI.
 Andersen, R.S., Peimankar, A. and Puthusserypady, S., 2019. A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications, 115, pp.465-473. (Web Link)
 Voskoboinik, A., Moskovitch, J., Plunkett, G., Bloom, J., Wong, G., Nalliah, C., Prabhu, S., Sugumar, H., Paramasweran, R., McLellan, A. and Ling, L.H., 2019. Cardioversion of atrial fibrillation in obese patients: Results from the Cardioversion‐BMI randomized controlled trial. Journal of cardiovascular electrophysiology, 30(2), pp.155-161. (Web Link)
 Rush, K.L., Hatt, L., Gorman, N., Janicki, L., Polasek, P. and Shay, M., 2019. Planning telehealth for older adults with atrial fibrillation in rural communities: understanding stakeholder perspectives. Clinical nursing research, 28(2), pp.130-149. (Web Link)
 Biobank-driven genomic discovery yields new insight into atrial fibrillation biology
Nature Geneticsvolume 50, pages1234–1239 (2018) (Web Link)
 Gramley, F., Vogt, F., Natour, M., Koellensperger, E. and Kettering, K. (2017) “Dronedarone after Catheter Ablation of Atrial Fibrillation: A New Option in Hybrid Therapy”, Cardiology and Angiology: An International Journal, 6(2), pp. 1-10. doi: 10.9734/CA/2017/32001. (Web Link)