Systematic review non-vitamin K oral anticoagulants in adults with congenital heart disease

Adults with congenital heart disease (ACHD) experience more thromboembolic complications than the general population. We systematically searched and critically appraised all studies on the safety and efficacy of non-vitamin-K oral anticoagulants (NOACs) in adult patients with various forms of congenital heart disease. PubMed and the Cochrane Central Register of Controlled Trials (CENTRAL) were used, with duplicate extraction of data and risk of bias assessment. The Newcastle-Ottawa quality assessment scale was used to assess study quality. Three studies fulfilled the inclusion criteria and were analyzed. The total number of participants was 766, with a total follow-up of 923 patient-years. The majority of patients (77%) received a NOAC for atrial arrhythmias, while the remainder were prescribed NOACs for secondary (19%) or primary (4%) thromboprophylaxis. The annual rate of thromboembolic and major bleeding events was low: 0.98% (95% CI: 0.51–1.86) and 1.74% (95% CI: 0.86–3.49) respectively. In Fontan patients, the annual rate of thromboembolic and major bleeding events was 3.13% (95% CI: 1.18–8.03) and 3.17% (95% CI: 0.15–41.39) respectively. NOACs appear safe and effective in ACHD without mechanical prostheses. Additional studies are, however, needed to confirm their efficacy in complex ACHD, especially those with a Fontan-type circulation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Arrhythmic risk stratification in heart failure mid-range ejection fraction patients with a non-invasive guiding to programmed ventricular stimulation two-step approach

Background: Although some post myocardial infarction (post-MI) and dilated cardiomyopathy (DCM) patients with mid-range ejection fraction heart failure (HFmrEF/40%-49%) face an increased risk for arrhythmic sudden cardiac death (SCD), current guidelines do not recommend an implantable cardiac defibrilator (ICD). We risk stratified hospitalized HFmrEF patients for SCD with a combined non-invasive risk factors (NIRFs) guiding to programmed ventricular stimulation (PVS) two-step approach. Methods: Forty-eight patients (male = 83%, age = 64 ± 14 years, LVEF = 45 ± 5%, CAD = 69%, DCM = 31%) underwent a NIRFs screening first-step with electrocardiogram (ECG), SAECG, Echocardiography and 24-hour ambulatory ECG (AECG). Thirty-two patients with presence of one of three NIRFs (SAECG ≥ 2 positive criteria for late potentials, ventricular premature beats ≥ 240/24 hours, and non-sustained ventricular tachycardia [VT] episode ≥ 1/24 hours) were further investigated with PVS. Patients were classified as either low risk (Group 1, n = 16, NIRFs−), moderate risk (Group 2, n = 18, NIRFs+/PVS−), and high risk (Group 3, n = 14, NIRFs+/PVS+). All in Group 3 received an ICD. Results: After 41 ± 18 months, 9 of 48 patients, experienced the major arrhythmic event (MAE) endpoint (clinical VT/fibrillation = 3, appropriate ICD activation = 6). The endpoint occurred more frequently in Group 3 (7/14, 50%) than in Groups 1 and 2 (2/34, 5.8%). Logistic regression model adjusted for PVS, age, and LVEF revealed that PVS was an independent MAE predictor (OR: 21.152, 95% CI: 2.618-170.887, P =.004). Kaplan-Meier curves diverged significantly (log rank, P <.001) while PVS negative predictive value was 94%. Conclusions: In hospitalized HFmrEF post-MI and DCM patients, a NIRFs guiding to PVS two-step approach efficiently detected the subgroup at increased risk for MAE. © 2020 The Authors. Journal of Arrhythmia published by John Wiley & Sons Australia, Ltd on behalf of Japanese Heart Rhythm Societ

Reappraising the role of class Ic antiarrhythmics in atrial fibrillation

Purpose: The objective of the present systematic review was to compare the effectiveness and safety of class Ic agents for cardioversion of paroxysmal atrial fibrillation (AF), in patients with and without structural heart disease (SHD). Methods: We focused on RCTs enrolling at least 50 adult patients with electrocardiogram-documented paroxysmal AF that compared either two pharmacological class Ic cardioversion agents (flecainide, propafenone), regardless of study design (parallel or crossover). We searched MEDLINE and the Cochrane Central Register of Controlled Trials. Initial search was performed from inception to 15 July 2021 with no language restrictions. Results: Intravenous flecainide is the most effective option for pharmacologic cardioversion of AF since only 2 patients need to be treated in order to cardiovert one more within 4 h. Most importantly, class Ic agents appear to be safe in the context of pharmacologic cardioversion of AF irrespective of the presence of SHD, pointing towards a possible reappraisal of the role in this setting. Conclusion: We suggest that class Ic agents (with flecainide appearing to be more effective) should be used for pharmacologic cardioversion in stable AF patients presenting in emergency department with unknown medical history, after excluding severe cardiac disease through a bedside examination. Registration number (DOI): Available in https://osf.io/apwt7/, https://doi.org/10.17605/OSF.IO/APWT7 © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature

Venous thromboembolism in the era of COVID-19

Coronavirus disease 2019 (COVID-19) does not only affect the respiratory system but appears to be a systemic disease. Venous thromboembolism is a common manifestation in hospitalized patients with COVID-19 with a reported incidence that is significantly higher compared to other acute viral infections. The pathophysiology mechanisms have not been fully explored and autopsy studies might enhance our understanding on this topic. Microthrombi formation occurs mainly in the pulmonary vasculature but can also occur in other organs. The high inflammatory burden related to COVID-19 seems to be associated with the coexisting coagulopathy. Concomitant manifestations of COVID-19, such as severe pneumonia, which has similar clinical presentation with pulmonary embolism (PE), and barriers related to strict isolation protocols are the two main reasons why PE diagnosis might be more challenging in patients with COVID-19. Medical societies have published guidance reports suggesting the administration of prophylactic anticoagulant therapy in hospitalized patients with COVID-19, but several questions regarding the optimal acute and long-term treatment of these patients remain unanswered. © The Author(s) 2020

Deep learning predicts heart failure with preserved, mid-range, and reduced left ventricular ejection fraction from patient clinical profiles

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention

Deep learning predicts heart failure with preserved, mid-range, and reduced left ventricular ejection fraction from patient clinical profiles.

Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF &gt; 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF &lt; 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p &lt; 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity &lt;5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention