Kam Cheong Wong1,2,3,4, Harry Klimis1,2,5, Nicole Lowres6, Amy von Huben1, Simone Marschner1, Clara K Chow1,2,5,6
With increasing use of handheld ECG devices for atrial fibrillation (AF) screening, it is important to understand their accuracy in community and hospital settings and how it differs among settings and other factors. A systematic review of eligible studies from community or hospital settings reporting the diagnostic accuracy of handheld ECG devices (ie, devices producing a rhythm strip) in detecting AF in adults, compared with a gold standard 12-lead ECG or Holter monitor, was performed. Bivariate hierarchical random-effects meta-analysis and meta-regression were performed using R V.3.6.0. The search identified 858 articles, of which 14 were included. Six studies recruited from community (n=6064 ECGs) and eight studies from hospital (n=2116 ECGs) settings. The pooled sensitivity was 89% (95% CI 81% to 94%) in the community and 92% (95% CI 83% to 97%) in the hospital. The pooled specificity was 99% (95% CI 98% to 99%) in the community and 95% (95% CI 90% to 98%) in the hospital. Accuracy of ECG devices varied: sensitivity ranged from 54.5% to 100% and specificity ranged from 61.9% to 100%. Meta-regression showed that setting (p=0.032) and ECG device type (p=0.022) significantly contributed to variations in sensitivity and specificity. The pooled sensitivity and specificity of single-lead handheld ECG devices were high. Setting and handheld ECG device type were significant factors of variation in sensitivity and specificity. These findings suggest that the setting including user training and handheld ECG device type should be carefully reviewed.
Alexandra Arvanitaki, Eleni Michou, Andreas Kalogeropoulos, Haralambos Karvounis, George Giannakoulas
Whereas up to about half of patients with heart failure with reduced ejection fraction (HFrEF) report no or only mild symptoms and are considered as clinically stable, the progressive nature of HFrEF, often silent, renders clinical stability a misleading situation, especially if disease progression is unrecognized. We highlight the challenges in the definition of clinical stability and mild symptomatic status in HFrEF, outline clinical characteristics and available diagnostic tools, and discuss evidence and gaps in the current guidelines for the management of these patients.
Methods and Results
This is a state‐of‐the‐art review that focuses on clinical, diagnostic, and therapeutic aspects in mildly symptomatic HFrEF patients; summarizes the challenges; and proposes directions for future research in this group of patients. The New York Heart Association classification has been widely used as a measure of prognosis in HFrEF, but it lacks objectivity and reproducibility in terms of symptoms assessment. The definition of clinical stability as described in current guidelines is vague and may often lead to underdiagnosis of disease progression in patients who appear to be ‘stable’ but in fact are at an increased risk of clinical worsening, hospitalization, or death. Although an increasing number of clinical trials proved that the efficacy of HFrEF therapies was unrelated to the symptomatic status of patients and led to their implementation early in the course of the disease, clinical inertia in terms of under‐prescription or underdosing of guideline‐recommended medications in mildly symptomatic HFrEF patients is still a challenging issue to deal with.
Mildly symptomatic status in a patient with HFrEF is very frequent; it should not be ignored and should not be regarded as an index of disease stability. The application of risk scores designed to predict mortality and mode of death should be engaged among mildly symptomatic patients, not only to identify the most suitable HF candidates for cardioverter defibrillator implantation, but also to identify patients who might benefit from early intensification of medical treatment before the implementation of more interventional approaches.
Harnessing cutting-edge technologies could bring new hope for people with heart and circulatory disease. In the first of a new series, Sarah Brealey learns how artificial intelligence could help treat heart failure.
Using artificial intelligence to make treatment predictions
We’re funding Dr Declan O’Regan and his colleagues at Imperial College London to see if artificial intelligence can make better predictions than doctors.
Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans.
We are good at diagnosis, but not so good at prognosis. We want to use machine learning to provide the missing piece of the puzzle.
Dr O’Regan wants to take that one step further. “We are good at diagnosis, but not so good at prognosis,” he says. “Making predictions about treatment that people with pulmonary hypertension will need is very difficult. We want to use machine learning to provide the missing piece of the puzzle.”
Computers can be ‘trained’ to make these predictions. You do this by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. In Dr O’Regan’s work, this information is heart scans, genetic and other test results, and how long each patient survived. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.
“We have trained the computer to recognise features of the heart, so when we give it scans it can analyse them,” Dr O’Regan says. “These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible.”
Next steps and patient involvement
After starting with pulmonary hypertension, Dr O’Regan and his team are now looking at dilated cardiomyopathy. “If these techniques work then they should apply to different types of heart disease,” he says. “At the moment it is predicting survival, but in future it could, for example, predict stroke or other cardiovascular events.”
What really matters to patients is finding the right information and the right treatment at the right time
The team tested the machine with data from previous patients. They compared the predictions with what actually happened, so they know that it works. The next step will be to test it with a different set of data. If that works, they can start a clinical trial of current patients, where the computer makes predictions and the team follows up to see if these were correct.
In 2017, Dr O’Regan and his colleagues met a group of heart patients who were enthusiastic about being involved in this project. “What really matters to them is finding the right information and the right treatment at the right time,” he says.
Right ventricular dilation and left ventricular diastolic impairment seem the most common findings from this small series.
Early on in the COVID-19 pandemic, we realized that the heart could be involved. Information is still emerging about the spectrum of cardiac disease. Investigators have now reported on echocardiographic findings from 100 patients in Israel hospitalized with COVID-19. (Of 112 original consecutive patients, 12 were excluded because of early hospital discharge, death soon after admission, or refusal to participate.) Initial echocardiograms were taken within 24 hours of admission.
The patients’ mean age was 66, and 63% were men. All patients were dyspneic, and 20% had abnormal troponin levels. Of the 100 people, 61 had mild disease, 29 moderate, and 10 severe; 32% had a normal echocardiogram at baseline. The most common abnormality was right ventricular dilation (39%), followed by left ventricular diastolic dysfunction (16%); 10% had left ventricular systolic dysfunction. Patients with higher-grade disease had shorter pulmonary acceleration time. The only echocardiographic parameter associated with worse outcome was left ventricular ejection fraction. In 12 patients with right ventricular dilation, a venous thrombosis was diagnosed in 5.
An overview of the cardiac and vascular impacts of Coronavirus, including STEMI, stroke, VTE, shock, heart failure, myocarditis and arrhythmias.
It was originally thought novel coronavirus (COVID-19, SARS-CoV-2) was primarily a respiratory disorder, but as larger numbers of patients contracted the virus, it quickly became clear it has many physiological manifestations. The impact of COVID-19 goes well beyond the lungs to impact the cardiovascular system and cause complications in the kidneys, brain and other organs, and critical patients often require care from a multidisciplinary care team.
This article offers an overview of cardiac and vascular complications of COVID-19 observed in the first six months of treating the new virus since the original outbreak in China, first reported in December 2019.
New study aims to address the myths and anxiety around caffeine consumption among heart rhythm patients
May 28, 2020 – A recent study revealed that drinking a couple of cups of coffee per day does not lead to a greater risk of arrhythmias. This potentially debunks a common myth that consuming caffeine in coffee and other drinks could lead to a faster heartbeat and the potential for a triggered arrhythmia for this patient population. Researchers analyzed several types of arrhythmias to better understand the impact of caffeine on this common heart condition.
Americans are dependent on their daily dose of caffeine, with more than 64 percent of Americans drinking a cup of coffee every day. Patients with arrhythmias are often cautioned against regular consumption by their doctors. Despite this specific concern, caffeine also offers health benefits, including antioxidants, improved metabolism, enhanced exercise performance, and increased alertness and concentration. Studies have also shown regular coffee consumption can also yield positive heart health results, lowering the risk of heart disease and stroke. With evidence showing caffeine’s positive impact on general heart health, there is a need to better understand the relationship between regular coffee consumption and arrhythmias.
The study reviewed coffee intake information and relevant data for 296,227 participants in the U.K. Biobank. The biobank follows the health and well-being of 500,000 volunteer participants and provides health information to researchers.
The mean age of participants was 56.69 ± 7.98 and 51.6 percent of participants were female. Investigators used multivariate Cox proportional hazards regression analysis to test the association between coffee consumption and arrhythmia risk and plotted a Kaplan-Meier curve for cumulative incidence of arrhythmia by coffee intake.
The results demonstrated an association between regular coffee consumption and a significantly lower risk of arrhythmias. Over 5.25 ± 21 years, there were 13,138 incident arrhythmias diagnosed, including 4,748 patients with atrial fibrillation or atrial flutter, 798 supraventricular tachycardia, 386 ventricular tachycardia and 308 premature ventricular complex. Compared with no consumption, coffee consumption of 1-2, 3-4, or 5 or greater cups a day was associated with a significantly lower risk of arrhythmia (HR 0.90, p<0.0001; HR 0.86, p<0.0001; HR 0.85 p=0.0005; HR 0.88, P=0.05, respectively). Each additional daily cup of coffee was associated with three percent lower incidence of arrhythmia.