As the coronavirus disease 19 (COVID-19) global pandemic rages across the globe, the race to prevent and treat this deadly disease has led to the "off-label" repurposing of drugs such as ...hydroxychloroquine and lopinavir/ritonavir, which have the potential for unwanted QT-interval prolongation and a risk of drug-induced sudden cardiac death. With the possibility that a considerable proportion of the world's population soon could receive COVID-19 pharmacotherapies with torsadogenic potential for therapy or postexposure prophylaxis, this document serves to help health care professionals mitigate the risk of drug-induced ventricular arrhythmias while minimizing risk of COVID-19 exposure to personnel and conserving the limited supply of personal protective equipment.
Abstract Background Dose reduction of non–vitamin K antagonist oral anticoagulants (NOACs) is indicated in patients with atrial fibrillation (AF) with renal impairment. Failure to reduce the dose in ...patients with severe kidney disease may increase bleeding risk, whereas dose reductions without a firm indication may decrease the effectiveness of stroke prevention. Objectives The goal of this study was to investigate NOAC dosing patterns and associated outcomes, i.e., stroke (ischemic stroke and systemic embolism) and major bleeding in patients treated in routine clinical practice. Methods Using a large U.S. administrative database, 14,865 patients with AF were identified who initiated apixaban, dabigatran, or rivaroxaban between October 1, 2010, and September 30, 2015. We examined use of a standard dose in patients with a renal indication for dose reduction (potential overdosing) and use of a reduced dose when the renal indication is not present (potential underdosing). Cox proportional hazards regression was performed in propensity score–matched cohorts to investigate the outcomes. Results Among the 1,473 patients with a renal indication for dose reduction, 43.0% were potentially overdosed, which was associated with a higher risk of major bleeding (hazard ratio: 2.19; 95% confidence interval: 1.07 to 4.46) but no statistically significant difference in stroke (3 NOACs pooled). Among the 13,392 patients with no renal indication for dose reduction, 13.3% were potentially underdosed. This underdosing was associated with a higher risk of stroke (hazard ratio: 4.87; 95% confidence interval: 1.30 to 18.26) but no statistically significant difference in major bleeding in apixaban-treated patients. There were no statistically significant relationships in dabigatran- or rivaroxaban-treated patients without a renal indication. Conclusions In routine clinical practice, prescribed NOAC doses are often inconsistent with drug labeling. These prescribing patterns may be associated with worse safety with no benefit in effectiveness in patients with severe kidney disease and worse effectiveness with no benefit in safety in apixaban-treated patients with normal or mildly impaired renal function.
Background The introduction of non-vitamin K antagonist oral anticoagulants (NOACs) has been a major advance for stroke prevention in atrial fibrillation (AF). Patients and clinicians now have a ...choice between different NOACs, but there is no direct comparative effectiveness evidence to guide decision-making. We aimed to compare the effectiveness and safety of dabigatran, rivaroxaban, and apixaban in clinical practice. Methods Using a large US administrative claims database, we created three one-to-one propensity-score-matched cohorts of patients with nonvalvular AF who were users of dabigatran, rivaroxaban, or apixaban between October 1, 2010 and February 28, 2015 (rivaroxaban vs dabigatran, n = 31,574; apixaban vs dabigatran, n = 13,084; and apixaban vs rivaroxaban, n = 13,130). The primary outcomes were stroke and systemic embolism (effectiveness) and major bleeding (safety) that occurred during treatment. Cox proportional hazards models were used to compare outcomes in propensity-score-matched cohorts. Results We found no differences between the three NOACs in the risk of stroke or systemic embolism (hazard ratio HR, 1.00; 95% CI, 0.75-1.32 for rivaroxaban vs dabigatran; HR, 0.82; 95% CI, 0.51-1.31 for apixaban vs dabigatran; and HR, 1.05; 95% CI, 0.64-1.72 for apixaban vs rivaroxaban). Apixaban was associated with a lower risk of major bleeding (HR, 0.50; 95% CI, 0.36-0.70; P < .001 vs dabigatran and HR, 0.39; 95% CI, 0.28-0.54; P < .001 vs rivaroxaban). Rivaroxaban was associated with an increased risk of major bleeding (HR, 1.30; 95% CI, 1.10-1.53; P < .01) and intracranial bleeding (HR, 1.79; 95% CI, 1.12-2.86; P < .05) compared with dabigatran. Conclusions Dabigatran, rivaroxaban, and apixaban appear to have similar effectiveness, although apixaban may be associated with a lower bleeding risk and rivaroxaban may be associated with an elevated bleeding risk.
Asymptomatic left ventricular dysfunction (ALVD) is present in 3-6% of the general population, is associated with reduced quality of life and longevity, and is treatable when found
. An inexpensive, ...noninvasive screening tool for ALVD in the doctor's office is not available. We tested the hypothesis that application of artificial intelligence (AI) to the electrocardiogram (ECG), a routine method of measuring the heart's electrical activity, could identify ALVD. Using paired 12-lead ECG and echocardiogram data, including the left ventricular ejection fraction (a measure of contractile function), from 44,959 patients at the Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7%, respectively. In patients without ventricular dysfunction, those with a positive AI screen were at 4 times the risk (hazard ratio, 4.1; 95% confidence interval, 3.3 to 5.0) of developing future ventricular dysfunction compared with those with a negative screen. Application of AI to the ECG-a ubiquitous, low-cost test-permits the ECG to serve as a powerful screening tool in asymptomatic individuals to identify ALVD.
Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is associated with substantial morbidity, mortality, and healthcare use. Great strides have been made in stroke prevention and ...rhythm control strategies, yet reducing the incidence of AF has been slowed by the increasing incidence and prevalence of AF risk factors, including obesity, physical inactivity, sleep apnea, diabetes mellitus, hypertension, and other modifiable lifestyle-related factors. Fortunately, many of these AF drivers are potentially reversible, and emerging evidence supports that addressing these modifiable risks may be effective for primary and secondary AF prevention. A structured, protocol-driven multidisciplinary approach to integrate lifestyle and risk factor management as an integral part of AF management may help in the prevention and treatment of AF. However, this aspect of AF management is currently underrecognized, underused, and understudied. The purpose of this American Heart Association scientific statement is to review the association of modifiable risk factors with AF and the effects of risk factor intervention. Implementation strategies, care pathways, and educational links for achieving impactful weight reduction, increased physical activity, and risk factor modification are included. Implications for clinical practice, gaps in knowledge, and future directions for the research community are highlighted.
Background & Aims Direct oral anticoagulant (DOAC) agents increase the risk of gastrointestinal (GI) bleeding. We investigated which DOAC had the most favorable GI safety profile and compared ...differences among these drugs in age-related risk of GI bleeding. Methods We conducted a retrospective, propensity-matched study using administrative claims data from the OptumLabs Data Warehouse of privately insured individuals and Medicare Advantage enrollees. We created 3 propensity-matched cohorts of patients with nonvalve atrial fibrillation with incident exposure to dabigatran, rivaroxaban, or apixaban from October 1, 2010 through February 28, 2015. We compared data on rivaroxaban vs dabigatran for 31,574 patients, data on apixaban vs dabigatran for 13,084 patients, and data on apixaban vs rivaroxaban for 13,130 patients. Cox proportional hazards models, stratified by age, were used to estimate rates of total GI bleeding. Results Baseline characteristics were well balanced among sub-cohorts. GI bleeding occurred more frequently in patients given rivaroxaban than dabigatran (hazard ratio HR, 1.20; 95% confidence interval CI, 1.00−1.45). Apixaban was associated with a lower risk of GI bleeding than dabigatran (HR, 0.39; 95% CI, 0.27−0.58; P < .001) or rivaroxaban (HR, 0.33; 95% CI, 0.22−0.49; P < .001). Rates of events for all DOACs increased among patients 75 years or older. Apixaban had a lower risk of association with GI bleeding in the very elderly than dabigatran (HR, 0.45; 95% CI, 0.29−0.71) or rivaroxaban (HR, 0.39; 95% CI, 0.25−0.61). Median times to GI bleeding were <90 days for apixaban and rivaroxaban and <120 days for dabigatran. Conclusions In a population-based study of patients receiving DOAC agents, we found apixaban had the most favorable GI safety profile and rivaroxaban least favorable. GI bleeding events among patient taking DOACs increased with age; the risk was greatest among persons ersons as old. Apixaban had the most favorable GI safety profile among all age groups.
Our understanding of the risk factors and complications of atrial fibrillation (AF) is based mostly on studies that have evaluated AF in a binary fashion (present or absent) and have not investigated ...AF burden. This scientific statement discusses the published literature and knowledge gaps related to methods of defining and measuring AF burden, the relationship of AF burden to cardiovascular and neurological outcomes, and the effect of lifestyle and risk factor modification on AF burden. Many studies examine outcomes by AF burden classified by AF type (paroxysmal versus nonparoxysmal); however, quantitatively, AF burden can be defined by longest duration, number of AF episodes during a monitoring period, and the proportion of time an individual is in AF during a monitoring period (expressed as a percentage). Current guidelines make identical recommendations for anticoagulation regardless of AF pattern or burden; however, a review of recent evidence suggests that higher AF burden is associated with higher risk of stroke. It is unclear whether the risk increases continuously or whether a threshold exists; if a threshold exists, it has not been defined. Higher burden of AF is also associated with higher prevalence and incidence of heart failure and higher risk of mortality, but not necessarily lower quality of life. A structured and comprehensive risk factor management program targeting risk factors, weight loss, and maintenance of a healthy weight appears to be effective in reducing AF burden. Despite this growing understanding of AF burden, research is needed into validation of definitions and measures of AF burden, determination of the threshold of AF burden that results in an increased risk of stroke that warrants anticoagulation, and discovery of the mechanisms underlying the weak temporal correlations of AF and stroke. Moreover, developments in monitoring technologies will likely change the landscape of long-term AF monitoring and could allow better definition of the significance of changes in AF burden over time.
The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular ...medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.
Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death.
This study sought to develop an artificial intelligence approach for the detection of HCM based on ...12-lead electrocardiography (ECG).
A convolutional neural network (CNN) was trained and validated using digital 12-lead ECG from 2,448 patients with a verified HCM diagnosis and 51,153 non-HCM age- and sex-matched control subjects. The ability of the CNN to detect HCM was then tested on a different dataset of 612 HCM and 12,788 control subjects.
In the combined datasets, mean age was 54.8 ± 15.9 years for the HCM group and 57.5 ± 15.5 years for the control group. After training and validation, the area under the curve (AUC) of the CNN in the validation dataset was 0.95 (95% confidence interval CI: 0.94 to 0.97) at the optimal probability threshold of 11% for having HCM. When applying this probability threshold to the testing dataset, the CNN’s AUC was 0.96 (95% CI: 0.95 to 0.96) with sensitivity 87% and specificity 90%. In subgroup analyses, the AUC was 0.95 (95% CI: 0.94 to 0.97) among patients with left ventricular hypertrophy by ECG criteria and 0.95 (95% CI: 0.90 to 1.00) among patients with a normal ECG. The model performed particularly well in younger patients (sensitivity 95%, specificity 92%). In patients with HCM with and without sarcomeric mutations, the model-derived median probabilities for having HCM were 97% and 96%, respectively.
ECG-based detection of HCM by an artificial intelligence algorithm can be achieved with high diagnostic performance, particularly in younger patients. This model requires further refinement and external validation, but it may hold promise for HCM screening.
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The possibilities and implementation of wearable cardiac monitoring beyond atrial fibrillation are increasing continuously. This review focuses on the real-world use and evolution of these devices ...for other arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The management of nonatrial fibrillation arrhythmias represents a broad field of wearable technologies in cardiology using Holter, event recorder, electrocardiogram (ECG) patches, wristbands and textiles. Implementation in other patient cohorts, such as ST-elevation myocardial infarction (STEMI), heart failure or sleep apnea, is feasible and expanding. In addition to appropriate accuracy, clinical studies must address the validation of clinical pathways including the appropriate device and clinical decisions resulting from the surrogate assessed.
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