Hellenic Journal of Cardiology最新文献

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).

Objective: This study aimed to leverage real-world electronic medical record data to develop interpretable machine learning models for diagnosis of Kawasaki disease while also exploring and prioritizing the significant risk factors.

Methods: A comprehensive study was conducted on 4087 pediatric patients at the Children's Hospital of Chongqing, China. The study collected demographic data, physical examination results, and laboratory findings. Statistical analyses were performed using IBM SPSS Statistics, Version 26.0. The optimal feature subset was used to develop intelligent diagnostic prediction models based on the Light Gradient Boosting Machine, Explainable Boosting Machine (EBM), Gradient Boosting Classifier (GBC), Fast Interpretable Greedy-Tree Sums, Decision Tree, AdaBoost Classifier, and Logistic Regression. Model performance was evaluated in three dimensions: discriminative ability via receiver operating characteristic curves, calibration accuracy using calibration curves, and interpretability through SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations).

Results: In this study, Kawasaki disease was diagnosed in 2971 participants. Analysis was conducted on 31 indicators, including red blood cell distribution width and erythrocyte sedimentation rate. The EBM model demonstrated superior performance relative to other models, with an area under the curve of 0.97, second only to the GBC model. Furthermore, the EBM model exhibited the highest calibration accuracy and maintained its interpretability without relying on external analytical tools such as SHAP and LIME, thus reducing interpretation biases. Platelet distribution width, total protein, and erythrocyte sedimentation rate were identified by the model as significant predictors for the diagnosis of Kawasaki disease.

Conclusion: This study used diverse machine learning models for early diagnosis of Kawasaki disease. The findings demonstrated that interpretable models such as EBM outperformed traditional machine learning models in terms of both interpretability and performance. Ensuring consistency between predictive models and clinical evidence is crucial for the successful integration of artificial intelligence into real-world clinical practice.

Objective: Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to investigate phenotypic heterogeneity in patients with Stanford type B aortic dissection (TBAD) through machine learning clustering analysis of cardiovascular computed tomography (CT) imaging.

Methods: Electronic medical records were collected to extract demographic and clinical features of patients with TBAD. Exclusion criteria ensured homogeneity and clinical relevance of the TBAD cohort. Controls were selected on the basis of age, comorbidity status, and imaging availability. Aortic morphological parameters were extracted from CT angiography and subjected to K-means clustering analysis to identify distinct phenotypes.

Results: Clustering analysis revealed three phenotypes of patients with TBAD with significant correlations with population characteristics and dissection rates. This pioneering study used CT-based three-dimensional reconstruction to classify high-risk individuals, demonstrating the potential of machine learning in enhancing diagnostic accuracy and personalized treatment strategies. Recent advancements in machine learning have garnered attention in cardiovascular imaging, particularly in aortic dissection research. These studies leverage various imaging modalities to extract valuable features and information from cardiovascular images, paving the way for more personalized interventions.

Conclusion: This study provides insights into the phenotypic heterogeneity of patients with TBAD using machine learning clustering analysis of cardiovascular CT imaging. The identified phenotypes exhibit correlations with population characteristics and dissection rates, highlighting the potential of machine learning in risk stratification and personalized management of aortic dissection. Further research in this field holds promise for improving diagnostic accuracy and treatment outcomes in patients with aortic dissection.

Objective: The echocardiographic assessment of the systemic right ventricle (sRV) performance during stress testing is limited and evaluation is not routinely performed. The aim of the study is to investigate sRV myocardial performance at rest and with exercise in patients with complete transposition of the great arteries (dTGA) who have undergone atrial switch operation.

Methods: In a single-center cross-sectional study, 41 patients with dTGA following the atrial switch operation and gender-matched 20 healthy volunteers underwent exercise echocardiography on a bicycle ergometer in the semi-supine position to assess sRV systolic function indices: tricuspid annular plane systolic excursion (TAPSE), right ventricular area change (FAC), global longitudinal strain (GLS) and systemic velocity time integral (VTI).

Results: Patients with sRV were characterized by lower systolic function assessed by TAPSE, s', FAC, GLS both at baseline and at peak exercise, compared with the control group. sRV GLS decreased during exercise in patients with sRV (-6 + 2.84) compared to increased in patients with systemic left ventricle (0.47 + 2.74), p < 0.001. There was no increase in VTI during exercise in patients with sRV, compared to controls (Δ VTI -0.01 ± 2.96 cm vs. Δ VTI 4.50 ± 3.13 cm, p < 0.001). There was a trend towards higher chronotropic incompetence in patients with sRV vs. control (61% vs. 45%, p = 0.28).

Conclusion: Our results confirmed that patients with dTGA have reduced ability to increase myocardial contractility and stroke volume during exercise. Chronotropic incompetence was prevalent in dTGA patients.

Objectives: Right heart catheterization (RHC) is a common diagnostic tool and of special importance in the diagnosis of pulmonary hypertension (PH). Until today, there have been no clear instructions or guidelines on which venous access to prefer. This meta-analysis assessed whether the choice of the venous access site for elective RHC has an impact on procedural or clinical outcomes.

Methods: A structured literature search was performed. Single-arm reports and controlled trials reporting event data were eligible. The primary endpoint was a composite of access-related and overall complications.

Results: Nineteen studies, including 6509 RHC procedures, were eligible. The results were analyzed in two groups. The first group compared central venous access (CVA; n = 2072) with peripheral venous access (PVA; n = 2680) and included only multi-arm studies (n = 12, C/P comparison). In the second group, all studies (n = 19, threeway comparison) were assessed to compare the three individual access ways. The overall complication rate was low at 1.0% (n = 68). The primary endpoint in the C/P comparison occurred significantly less for PVA than for CVA (0.1% vs. 1.2%; p = 0.004). In the threeway comparison, PVA had a significantly lower complication rate than femoral access (0.3% vs. 1.1%; p = 0.04). Jugular access had the numerically highest complication rate (2.0%), but the difference was not significant compared to peripheral (0.3%; p = 0.29) or femoral access (1.1%; p = 0.32).

Conclusion: This meta-analysis showed that PVA for RHC has a significantly lower complication rate than CVA. There was a low level of certainty and high heterogeneity. This pooled data analysis indicated PVA as the primary venous access for RHC.

Objective: Bicuspid aortic valve (BAV) is prone to promote left ventricular remodeling (LVR), which is associated with adverse clinical outcomes. Although the association between angiogenic activity and LVR has been established, pro-angiogenic cytokine features and potential biomarker candidates for LVR in patients with BAV remain to be clarified.

Methods: From November 2018 to May 2019, patients with BAV diagnosed by transthoracic echocardiography at our institution were included. LVR was diagnosed on the basis of echocardiographic calculations of relative wall thickness (RWT) and left ventricular mass index (LVMI). A multiplex ELISA array was used to measure the plasma levels of 60 angiogenesis-related cytokines.

Results: Among 103 patients with BAV, 71 were categorized into the LVR group and 32 into the normal left ventricular (LV) geometry group. BAV patients with LVR demonstrated increased LVMI, elevated prevalence of moderate to severe aortic stenosis and aortic regurgitation, and decreased LV ejection fraction (LVEF). Plasma levels of angiopoietin-1 were elevated in BAV patients with or without LVR compared with healthy controls (P = 0.001, P < 0.001, respectively), and were negatively correlated with RWT (r = -0.222, P = 0.027). Plasma levels of angiopoietin-2 were elevated in the LVR group (P = 0.001) compared with the normal LV geometry group, and were negatively correlated with LVEF (r = -0.330, P = 0.002).

Conclusion: Decreased angiogenesis plays a crucial role in the occurrence and progression of LVR in patients with BAV. Disturbance in the pro- and anti-angiogenesis equilibrium in BAV patients with LVR may reflect the aggravation of endothelial injury and dysfunction.