Electrocardiographic Features of Left Ventricular Diastolic Dysfunction and Heart Failure With Preserved Ejection Fraction: A Systematic Review

Background: Electrocardiographic features are well-known for heart failure with reduced ejection fraction (HFrEF), but not for left ventricular diastolic dysfunction (LVDD) and heart failure with preserved ejection fraction (HFpEF). As ECG features could help to identify high-risk individuals in primary care, we systematically reviewed the literature for ECG features diagnosing women and men suspected of LVDD and HFpEF.Methods and Results: Among the 7,127 records identified, only 10 studies reported diagnostic measures, of which 9 studied LVDD. For LVDD, the most promising features were T-end-P/(PQ*age), which is the electrocardiographic equivalent of the passive-to-active filling (AUC: 0.91–0.96), and repolarization times (QTc interval ≥ 350 ms, AUC: 0.85). For HFpEF, the Cornell product ≥ 1,800 mm*ms showed poor sensitivity of 40% (AUC: 0.62). No studies presented results stratified by sex.Conclusion: Electrocardiographic features are not widely evaluated in diagnostic studies for LVDD and HFpEF. Only for LVDD, two ECG features related to the diastolic interval, and repolarization measures showed diagnostic potential. To improve diagnosis and care for women and men suspected of heart failure, reporting of sex-specific data on ECG features is encouraged.

Deep Learning-Based Heart Sound Analysis for Left Ventricular Diastolic Dysfunction Diagnosis

The aggravation of left ventricular diastolic dysfunction (LVDD) could lead to ventricular remodeling, wall stiffness, reduced compliance, and progression to heart failure with a preserved ejection fraction. A non-invasive method based on convolutional neural networks (CNN) and heart sounds (HS) is presented for the early diagnosis of LVDD in this paper. A deep convolutional generative adversarial networks (DCGAN) model-based data augmentation (DA) method was proposed to expand a HS database of LVDD for model training. Firstly, the preprocessing of HS signals was performed using the improved wavelet denoising method. Secondly, the logistic regression based hidden semi-Markov model was utilized to segment HS signals, which were subsequently converted into spectrograms for DA using the short-time Fourier transform (STFT). Finally, the proposed method was compared with VGG-16, VGG-19, ResNet-18, ResNet-50, DenseNet-121, and AlexNet in terms of performance for LVDD diagnosis. The result shows that the proposed method has a reasonable performance with an accuracy of 0.987, a sensitivity of 0.986, and a specificity of 0.988, which proves the effectiveness of HS analysis for the early diagnosis of LVDD and demonstrates that the DCGAN-based DA method could effectively augment HS data.

Association of Body Mass Index with Left Ventricular Diastolic Dysfunction Among Ambulatory Patients with Diabetes Mellitus in Rural Uganda: A Cross Sectional Study.

Background: Left ventricular diastolic dysfunction (LVDD) is a recognized complication of diabetes mellitus which precedes and is a risk factor for heart failure. We aimed to determine the prevalence of LVDD and its association with body mass index (BMI) in ambulatory adults with diabetes mellitus. Methods: We conducted a cross-sectional study of 195 ambulatory Ugandan adults living with diabetes mellitus for at least five years from the time of diagnosis at Mbarara Regional Referral Hospital (MRRH). We collected demographic, clinical data and measured body mass index. Laboratory tests included glycated hemoglobin (HbA1c), low-density lipoprotein (LDL), and urine microalbumin. Echocardiography was done to determine LVDD by assessing the mitral inflow E/A ratio, E/è ratio, TR jet peak velocity, and left atrium maximum volume index. Logistic regression was used to establish associations of body mass index and other covariates with LVDD. Results: Overall, 195 participants were enrolled and 141(72.3%) were females. The mean age was 62 [standard deviation (SD), 11.5] years and median duration of diabetes diagnosis was 10 [Interquartile range (IQR), 7,15] years. LVDD was prevalent at 86% and majority, 127(65.13%) had grade-1 diastolic dysfunction. BMI ≥25kg/m2 [adjusted Odds Ratio (aOR)=2.8, (95% confidence interval (CI)=1-7.6), p=0.038], age 50 year or more [aOR= 4.9, (95%CI=1.5-16.2), p=0.010], and history of hypertension [aOR= 3.0, (95% CI= 1.1-8.1), p=0.031] were associated with LVDD.Conclusion: The prevalence of LVDD was high during the study period. We recommend early and periodic echocardiographic screening for diabetes patients with high body mass index.

Characteristics and outcomes of hospitalized patients with cardiovascular complications of COVID-19

Introduction: To address cardiovascular (CV) complications and their relationship to clinical outcomes in hospitalized patients with COVID-19. Methods: A total of 196 hospitalized patients with COVID-19 were enrolled in this retrospective single-center cohort study from September 10, 2020, to December 10, 2020, with a median age of 65 years (IQR, 52-77). Follow-up continued for 3 months after hospital discharge. Results: CV complication was observed in 54 (27.6%) patients, with arrhythmia being the most prevalent (14.8%) followed by myocarditis, acute coronary syndromes, ST-elevation myocardial infarction, cerebrovascular accident, and deep vein thrombosis in 15 (7.7%), 12 (6.1%), 10(5.1%), 8 (4.1%), and 4 (2%) patients, respectively. The proportion of patients with elevated high-sensitivity troponin I, N-terminal pro-B-type natriuretic peptide, left ventricular diastolic dysfunction, and heart failure with preserved ejection fraction was greater in the CV complication group. Severe forms of COVID-19 comprised nearly two-thirds (64.3%) of our study population and constituted a significantly higher share of the CV complication group members (75.9%vs 59.9%; P=0.036). Intensive care unit admission (64.8% vs 44.4%; P=0.011) and stay (5.5days vs 0 day; P=0.032) were notably higher in patients with CV complications. Among 196patients, 50 died during hospitalization and 10 died after discharge, yielding all-cause mortality of 30.8%. However, there were no between-group differences concerning mortality. Age, heart failure, cancer/autoimmune disease, disease severity, interferon beta-1a, and arrhythmia were the independent predictors of all-cause mortality during and after hospitalization. Conclusion: CV complications occurred widely among COVID-19 patients. Moreover,arrhythmia, as the most common complication, was associated with increased mortality.