Hellenic Journal of Cardiology最新文献

Background: Left ventricular thrombus (LVT) can develop in a diverse group of patients with various underlying causes, resulting in divergent natural histories and trajectories with treatment. Our aim was to use cluster analysis to identify unique clinical profiles among patients with LVT and then compare their clinical characteristics, treatment strategies, and outcomes.

Methods: We conducted a retrospective study involving 472 patients with LVT whose data were extracted from a tertiary center's echocardiography database, from March 2011 to January 2021. We used the TwoStep cluster analysis method, examining 19 variables.

Results: Our analysis of the 472 patients with LVT revealed two distinct patient clusters. Cluster 1, comprising 247 individuals (52.3%), was characterized by younger patients with a lower incidence of traditional cardiovascular risk factors and relatively fewer comorbidities compared with Cluster 2. Most patients had LVT attributed to an underlying ischemic condition, with a larger proportion being due to post-acute myocardial infarction in Cluster 1 (68.8%), and due to ischemic cardiomyopathy in Cluster 2 (57.8%). Notably, patients in Cluster 2 exhibited a reduced likelihood of LVT resolution (hazard ratio [HR] 0.58, 95% confidence interval [CI] 0.44-0.77, p < 0.001) and a higher risk of all-cause mortality (HR 2.27, 95% CI 1.43-3.60, p = 0.001). These associations persisted even after adjusting for variables such as anticoagulation treatment, the presence of left ventricular aneurysms, and specific LVT characteristics such as mobility, protrusion, and size.

Conclusion: Through TwoStep cluster analysis, we identified two distinct clinical phenotypes among patients with LVT, each distinguished by unique baseline clinical attributes and varying prognoses.

Background: Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features.

Methods: Precision of ECG classification through hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and Variational Autoencoder (VAE) techniques. By combining these methods, we aim to achieve more accurate and robust ECG interpretation. The method is trained and tested over PTB-XL dataset, which contains 21,799 with 12-lead ECGs from 18,869 patients, each spanning 10 seconds. The classification evaluation of 5 super-classes and 23 sub-classes of CVD, with the proposed CNN-VAE model is compared.

Results: The classification of various CVD had resulted with the highest accuracy of 98.51%, specificity of 98.12%, sensitivity 97.9% and F1-score 97.95%. We have also achieved the minimum false positive and false negative rates as 2.07 and 1.87 respectively during validation. The results are validated upon the annotations given by individual cardiologists, who assigned potentially multiple ECG statements to each record.

Conclusion: When compared to other deep learning methods, our suggested CNN-VAE model performs significantly better in testing phase. This study proposes a new architecture of combining CNN-VAE for CVD classification from ECG data, this can help the clinicians to identify the disease earlier and carry further treatment. The CNN-VAE model can better characterize input signals due to its hybrid architecture.

Objective: This nationwide study aims to analyze mortality trends for all individual causes in Greece from 2001 to 2020, with a specific focus on 2020, a year influenced by the COVID-19 pandemic. As Greece is the fastest-aging country in Europe, the study's findings can be generalized to other aging societies, guiding the reevaluation of global health policies.

Methods: Data on the population and the number of deaths were retrieved from the Hellenic Statistical Authority. We calculated age-standardized mortality rates (ASMR) and cause-specific mortality rates by sex in three age groups (0-64, 65-79, and 80+ years) from 2001 to 2020. Proportional mortality rates for 2020 were determined. Statistical analysis used generalized linear models with Python Programming Language.

Results: From 2001 to 2020, the ASMR of cardiovascular diseases (CVD) decreased by 42.7% (p < 0.0001), with declines in most sub-causes, except for hypertensive diseases, which increased by 2.8-fold (p < 0.0001). In 2020, the proportional mortality rates of the three leading causes were 34.9% for CVD, 23.5% for neoplasms, and 9.6% for respiratory diseases (RD). In 2020, CVD were the leading cause of death among individuals aged 80+ years (39.3%), while neoplasms were the leading cause among those aged 0-79 years (37.7%). Among cardiovascular sub-causes, cerebrovascular diseases were predominant in the 80+ year age group (30.3%), while ischemic heart diseases were most prevalent among those aged 0-79 years (up to 60.0%).

Conclusions: The global phenomenon of population aging necessitates a reframing of health policies in our aging societies, focusing on diseases with either a high mortality burden, such as CVD, neoplasms, and RD, or those experiencing increasing trends, such as hypertensive diseases.

Background: We assessed left ventricular ejection fraction (LVEF) to compare the effects of renin-angiotensin system inhibitors (RASI) in patients with non-ST-segment elevation myocardial infarction (NSTEMI).

Methods: We categorized 4558 patients with NSTEMI as either RASI users (3752 patients) or non-users (806 patients). The 3-year patient-oriented composite outcome (POCO), which included all-cause death, recurrent myocardial infarction, any repeat revascularization, or hospitalization for heart failure (HF), was the primary outcome. To compare clinical outcomes, a multivariable-adjusted hazard ratio (aHR) was calculated after performing multicollinearity tests on all significant confounding variables (P < 0.05).

Results: Among RASI users, the aHRs for POCO, all-cause death, and cardiac death were significantly higher in the HF with reduced EF (HFrEF) subgroup than in the HF with mildly reduced EF (HFmrEF) (1.610, 2.120, and 2.489; P < 0.001, <0.001, and <0.001; respectively) and HF with preserved EF (HFpEF) (2.234, 3.920, and 5.215; P < 0.001, <0.001, and <0.001; respectively) subgroups. The aHRs for these variables were significantly higher in the HFmrEF subgroup than the HFpEF subgroup (1.416, 1.843, and 2.172, respectively). Among RASI non-users, the aHRs for these variables were significantly higher in the HFrEF subgroup than the HFmrEF (2.573, 3.172, and 3.762, respectively) and HFpEF (2.425, 3.805, and 4.178, respectively) subgroups. In three LVEF subgroups, RASI users exhibited lower aHRs for POCO and all-cause death than RASI non-users.

Conclusion: In the RASI users group, the aHRs for POCO and mortality were highest in the HFrEF subgroup, intermediate in the HFmrEF subgroup, and lowest in the HFpEF subgroup.

Objective: The benefits of rhythm control in early atrial fibrillation (AF) are increasingly recognized. This study aimed to investigate whether early AF ablation contributes to long-term sinus rhythm maintenance and to identify a suitable predictive score.

Methods: According to diagnosis-to-ablation time, this study prospectively enrolled 245 patients with very early AF, 262 with early AF, and 588 with late AF for radiofrequency ablation from June 2017 to December 2022. Clinical data, risk scores, and follow-up results were collected and analyzed.

Results: Baseline characteristics were similar among the three cohorts. During a median follow-up period of 26 months, AF recurrence was observed in 61 (24.9%), 66 (25.2%), and 216 (36.7%) patients in the very early, early, and late AF cohorts, respectively. In the multivariable-adjusted model, very early and early AF were associated with a reduced risk of AF recurrence, with hazard ratios of 0.72 (95% confidence interval [CI] 0.52-0.99) and 0.57 (95% CI 0.41-0.78), respectively. The APPLE score demonstrated the highest predictive power for very early AF, with an area under the curve (AUC) of 0.74. However, its predictive power decreased with time from diagnosis, showing low predictive power for late AF (AUC = 0.58). In addition, the time-dependent concordance index showed consistent results. For very early AF, the Akaike information criterion and decision curve analysis showed that APPLE had the highest predictive value.

Conclusion: Very early AF ablation was associated with a lower recurrence rate, and the APPLE score provided a higher predictive value for these patients. (URL: https://www.chictr.org.cn/; Unique identifier: ChiCTR-OIN-17013021).