Early Rhythm-Control Therapy in Patients with Atrial Fibrillation

P. Kirchhof, A.J. Camm, A. Goette, A. Brandes, L. Eckardt, A. Elvan, T. Fetsch, I.C. van Gelder, D. Haase, L.M. Haegeli, F. Hamann, H. Heidbüchel, G. Hindricks, J. Kautzner, K.-H. Kuck, L. Mont, G.A. Ng, J. Rekosz, N. Schoen, U. Schotten, A. Suling, J. Taggeselle, S. Themistoclakis, E. Vettorazzi, P. Vardas, K. Wegscheider, S. Willems, H.J.G.M. Crijns, and G. Breithardt, for the EAST-AFNET 4 Trial Investigators*

n engl j med 383;14 nejm.org October 1, 2020


Despite improvements in the management of atrial fibrillation, patients with this
condition remain at increased risk for cardiovascular complications. It is unclear whether early rhythm-control therapy can reduce this risk.

In this international, investigator-initiated, parallel-group, open, blinded outcome assessment trial, we randomly assigned patients who had early atrial fibrillation
(diagnosed ≤1 year before enrollment) and cardiovascular conditions to receive
either early rhythm control or usual care. Early rhythm control included treatment
with anti-arrhythmic drugs or atrial fibrillation ablation after randomization.
Usual care limited rhythm control to the management of atrial fibrillation–related
symptoms. The first primary outcome was a composite of death from cardiovascular
causes, stroke, or hospitalization with worsening of heart failure or acute coronary syndrome; the second primary outcome was the number of nights spent in the hospital per year. The primary safety outcome was a composite of death, stroke, or serious adverse events related to rhythm-control therapy. Secondary outcomes, including symptoms and left ventricular function, were also evaluated.

In 135 centers, 2789 patients with early atrial fibrillation (median time since diagnosis,
36 days) underwent randomization. The trial was stopped for efficacy at the third interim analysis after a median of 5.1 years of follow-up per patient. A first-primary-outcome event occurred in 249 of the patients assigned to early rhythm control (3.9 per 100 person-years) and in 316 patients assigned to usual care (5.0 per 100 person-years) (hazard ratio, 0.79; 96% confidence interval, 0.66 to 0.94; P = 0.005). The mean (±SD) number of nights spent in the hospital did not differ significantly between the groups (5.8±21.9 and 5.1±15.5 days per year, respectively; P = 0.23). The percentage of patients with a primary safety outcome
event did not differ significantly between the groups; serious adverse events related to rhythm-control therapy occurred in 4.9% of the patients assigned to early rhythm control and 1.4% of the patients assigned to usual care. Symptoms and left ventricular function at 2 years did not differ significantly between the groups.

Early rhythm-control therapy was associated with a lower risk of adverse cardiovascular
outcomes than usual care among patients with early atrial fibrillation and
cardiovascular conditions. (Funded by the German Ministry of Education and
Research and others; EAST-AFNET 4 ISRCTN number, ISRCTN04708680; Clinical-
Trials.gov number, NCT01288352; EudraCT number, 2010 – 021258 – 20.)

Myocardial fibrosis in asymptomatic and symptomatic chronic severe primary mitral regurgitation and relationship to tissue characterisation and left ventricular function on cardiovascular magnetic resonance

Boyang Liu, Desley A. H. Neil, Monisha Premchand, Moninder Bhabra, Ramesh Patel, Thomas Barker, Nicolas Nikolaidis, J. Stephen Billing, Thomas A. Treibel, James C. Moon, Arantxa González, James Hodson, Nicola C. Edwards and Richard P. Steeds

Liu et al. J Cardiovasc Magn Reson (2020) 22:86


Background: Myocardial fibrosis occurs in end-stage heart failure secondary to mitral regurgitation (MR), but it is not known whether this is present before onset of symptoms or myocardial dysfunction. This study aimed to characterise myocardial fibrosis in chronic severe primary MR on histology, compare this to tissue characterisation on cardiovascular magnetic resonance (CMR) imaging, and investigate associations with symptoms, left ventricular (LV) function, and exercise capacity.

Methods: Patients with class I or IIa indications for surgery underwent CMR and cardiopulmonary exercise testing. LV biopsies were taken at surgery and the extent of fibrosis was quantified on histology using collagen volume fraction (CVFmean) compared to autopsy controls without cardiac pathology.

Results: 120 consecutive patients (64±13 years; 71% male) were recruited; 105 patients underwent MV repair while 15 chose conservative management. LV biopsies were obtained in 86 patients (234 biopsy samples in total). MR patients had more fibrosis compared to 8 autopsy controls (median: 14.6% [interquartile range 7.4–20.3] vs. 3.3% [2.6–6.1], P<0.001); this difference persisted in the asymptomatic patients (CVFmean 13.6% [6.3–18.8], P<0.001), but severity of fibrosis was not significantly higher in NYHA II-III symptomatic MR (CVFmean 15.7% [9.9–23.1] (P=0.083). Fibrosis was patchy across biopsy sites (intraclass correlation 0.23, 95% CI 0.08–0.39, P=0.001). No significant relationships were identified between CVFmean and CMR tissue characterisation [native T1, extracellular volume (ECV) or late gadolinium enhancement] or measures of LV function [LV ejection fraction (LVEF), global longitudinal strain (GLS)]. Although the range of ECV was small (27.3±3.2%), ECV correlated with multiple measures of LV function (LVEF: Rho=−0.22, P=0.029, GLS: Rho=0.29, P=0.003), as well as NTproBNP (Rho=0.54, P<0.001) and exercise capacity (%PredVO2max: R=−0.22, P=0.030).


Patients with chronic primary MR have increased fibrosis before the onset of symptoms. Due to the patchy nature of fibrosis, CMR derived ECV may be a better marker of global myocardial status.

A machine learning algorithm supports ultrasound‑naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF

Matthias Schneider · Philipp Bartko · Welf Geller · Varius Dannenberg · Andreas König · Christina Binder · Georg Goliasch · Christian Hengstenberg · Thomas Binder

Department of Internal Medicine II, Medical University of Vienna, Waehringer Guertel 18‑20, 1090 Vienna, Austria

The International Journal of Cardiovascular Imaging

Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the captured apical-4-chamber (AP4), apical-2-chamber (AP2), and parasternal-long-axis (PLAX) loops. We sought to test this algorithm by having first-year medical students without previous ultrasound knowledge scan real patients. Nineteen echo-naïve first-year medical students were trained in the basics of echocardiography by a 2.5 h online video tutorial. Each student then scanned three patients with the help of the AI. Image quality was graded according to the American College of Emergency Physicians scale. If rated as diagnostic quality, the AI calculated LVEF from the acquired loops (monoplane and also a “best-LVEF” considering all views acquired in the particular
patient). These LVEF calculations were compared to images of the same patients captured and read by three experts (ground-truth LVEF [GT-EF]). The novices acquired diagnostic-quality images in 33/57 (58%), 49/57 (86%), and 39/57 (68%) patients in the PLAX, AP4, and AP2, respectively. At least one of the three views was obtained in 91% of the attempts.

We found an excellent agreement between the machine’s LVEF calculations from images acquired by the novices with the GT-EF (bias of 3.5% ± 5.6 and r = 0.92, p < 0.001 in the “best-LVEF” algorithm). This pilot study shows first evidence that a machine-learning algorithm can guide ultrasound-novices to acquire diagnostic echo loops and provide an automated LVEF calculation that is in agreement with a human expert.

Cryoablation or Drug Therapy for Initial Treatment of Atrial Fibrillation

Jason G. Andrade, M.D., George A. Wells, Ph.D., Marc W. Deyell, M.D., Matthew Bennett, M.D., Vidal Essebag, M.D., Ph.D., Jean Champagne, M.D., Jean-Francois Roux, M.D., Derek Yung, M.D., Allan Skanes, M.D., Yaariv Khaykin, M.D., Carlos Morillo, M.D., Umjeet Jolly, M.D., et al., for the EARLY-AF Investigators

The New England Journal of Medicine, November 16, 2020
DOI: 10.1056/NEJMoa2029980

Continue reading “Cryoablation or Drug Therapy for Initial Treatment of Atrial Fibrillation”

A Socio-Technological Sales Framework in a virtual world

The COVID-19 pandemic and consequent border lockdowns have brought immediate and wide-ranging challenges for most B2B sales industries. This is even more amplified for those of us in the healthcare industry, whereby hospitals have in many states excluded external contractors from entering their sites. For most companies, the challenge of adapting to the new normal has required adaptation from traditional face-to-face sales methodology with less opportunities to make those doctor calls, and no conferences, trade exhibits or clinical meetings other than virtual meetings to get the attention of the busy specialist.

Introducing new technology to the cardiology sector has a long lead time in the best of circumstances as most cardiologists and technicians often are not early adopters of new technology and we’d argue, nor would you want them to be. Our Federal Government has encouraged investment into business through utilisation of the increased $150,000 instant asset write off tax scheme. It is an opportune time to invest in better technology that drive improved patient outcomes.

person using macbook

Cardio-Jenic, as an innovation company specialising in new technology, has had to adapt to embrace a new Socio-Technological Sales Framework that in the long term may even have greater benefits for our customers. Web-based video conferencing platforms like Zoom allows the specialist or technical staff to choose meeting times more suited to their busy schedules, rather than the traditional rep call. Many of our cloud-based solutions allow full customer access to enhance their user experience as they get to “play” with the technology in their time. Remote desktop software such as TeamViewer can also support the sales process as well as allowing remote access for troubleshooting if required. We have also completely “tooled” up various laptops as virtual sales assistants, that we can express to your surgery for a fuller user experience.

As an innovation company, Cardio-Jenic has taken advantage of varying technologies that allows us to demo new technology throughout Australia, in your time at your pace. Many of our customers from Perth to North Queensland have already benefited from our virtual sales approach.

In your time, at your pace

For more information please contact Carolyn Jensen on

cjensen@cardio-jenic.com or 0401 305 977.

An optimization study of the ultra-short period for HRV analysis at rest and post-exercise

Liang, Wu; Ping,Shi; Hongliu, Yu; Yang, Liu

Journal of Electrocardiology
Volume 63, November–December 2020, Pages 57-63


Continue reading “An optimization study of the ultra-short period for HRV analysis at rest and post-exercise”

The Effect of Resistance Training on Serum Levels of NT-proBNP, GDF-15, and Markers of Cardiac Damage in the Elderly Males

Ebrahim Rangraz, Bahman Mirzaei, Farhad Rahmani Nia

International Journal of Applied Exercise Physiology
2322-3537 Vol.8 No.1 http://www.ijaep.com

DOI: 10. 30472 /ijaep.v8i1.329

Continue reading “The Effect of Resistance Training on Serum Levels of NT-proBNP, GDF-15, and Markers of Cardiac Damage in the Elderly Males”

Left Atrial Strain as a Predictor of New-Onset Atrial Fibrillation in Patients With Heart Failure

Jin Joo Park, Jae-Hyeong Park, In-Chang Hwang, Jun-Bean Park, Goo-Yeong Cho and Thomas H. Marwick

JACC: Cardiovascular Imaging; Volume 13, Issue 10, October 2020 DOI: 10.1016/j.jcmg.2020.04.031

Continue reading “Left Atrial Strain as a Predictor of New-Onset Atrial Fibrillation in Patients With Heart Failure”

Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers

State-of-the-Art Review

Ashlee Davis BS, ACS, RDCS, FASE; Kristen Billick BS, ACS, RDCS, FASE; Kenneth Horton ACS, RCS, RCIS, FASE; Madeline Jankowski BS, RDCS, FASE; Peg Knoll RDCS, RCS, FASE; Jane E.Marshall BS, RDCS, FASE; Alan Paloma RDCS; Richie Palma BS, ACS, RDCS, FASE; David B.Adams ACS, RCS, RDCS, FASE



AI will have a strong role in echocardiography.

AI will guide image acquisition and optimization.

AI for image analysis may aid in interpretation.

AI is a tool that will not replace sonographers but will help them be more efficient.

Continue reading “Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers”