Background: Heart rate turbulence (HRT) is a non-invasive technique that can be used to evaluate autonomic nervous system (ANS) function and cardiac arrhythmia. The objective of this study is to investigate whether COVID-19 can lead to long-term blunted HRT following recovery. Methods: This retrospective cohort study included 253 individuals with a confirmed history of COVID-19, referred to as the recovered COVID-19 group, along with 315 healthy participants who had no history of the virus. The recovered COVID-19 group was categorized into three subgroups based on their chest CT severity scores. The HRT analyses were obtained from a 24-h electrocardiography-Holter recording. Results: This study revealed that the HRT onset value was elevated in the recovered COVID-19 group, while the HRT slope value showed a significant decrease when compared to the control group. Correlation analyses indicated a positive relationship between the chest CT severity score and HRT onset, whereas a negative correlation was observed between the chest CT severity score and HRT slope. Regression analyses identified recovery from severe COVID-19, chest CT severity score, hypertension (HT), and smoking as independent predictors of both abnormal HRT onset and the existence of an abnormal HRT slope. Conclusions: Individuals who have recovered from severe COVID-19 are expected to encounter a permanent blunting of HRT, which is regarded as a significant indicator of an increased risk of ventricular arrhythmias and impaired autonomic nervous system (ANS) function. Recovered severe COVID-19 individuals should be carefully evaluated for HRT with 24-h ECG-Holter.

Aim: One of the primary contributors of the development of atrial fibrillation (AF) is autonomic dysfunction. It is suggested that heart rate turbulence (HRT) be utilized to assess autonomic nervous system (ANS) function. In this research, we analyzed the impact of COVID-19 on atrial fibrillation predisposition in the post-recovery period by analyzing atrial HRT measurements of patients who described palpitations in the post-COVID-19 period. Methods: This study included 407 healthy participants without a positive COVID-19 record and 328 patients having a verified positive COVID-19 record (recovered COVID-19). All subjects were categorized into four groups (controls, recovered mild COVID-19, recovered moderate COVID-19 and recovered severe COVID-19) based on the severity value of their chest CT scan. The atrial HRT analyses were taken from a 24-hour electrocardiography- Holter recording. Results: This study revealed that atrial HRT Onset values were significantly higher in the recovered severe COVID-19 group than the other groups. A positive association was found between the atrial HRT onset value and the chest CT severity value. However, no relationship was found between the atrial HRT onset value and the number of positive PCR test for COVID-19 or the amount of time that had passed since COVID-19. Regression analysis showed that the chest CT severity score, HT, smoking, and recovery from severe COVID-19, were independent predictors of atrial HRT Onset values and abnormal atrial HRT Onset existence. Conclusion: Blunted atrial HRT, considered a significant predisposition to the development of atrial fibrillation, is more likely to occur in people who recovered from serious COVID-19 than in individuals who have never had severe COVID-19. These individuals should be carefully evaluated for atrial HRT.

Background/Objectives: Behçet’s disease (BD) is a chronic multisystem inflammatory vasculitis associated with an increased risk of ventricular arrhythmias and sudden cardiac death. The index of cardiac electrophysiological balance (iCEB) and its corrected form (iCEBc) are novel electrocardiographic markers reflecting the balance between ventricular depolarization and repolarization and have been proposed as indicators of arrhythmic risk. This study aimed to evaluate iCEB and iCEBc in patients with BD and to investigate their associations with disease duration and inflammatory activity. Methods: This observational, cross-sectional study included 81 patients with clinically inactive BD (Behçet’s Disease Current Activity Form score 0–1) and 84 age- and sex-matched healthy controls. All participants underwent laboratory testing, transthoracic echocardiography and standard 12-lead electrocardiography. QT, QTc, QRS, iCEB (QT/QRS), and iCEBc (QTc/QRS) values were calculated. Correlation analyses were performed to evaluate the relationships between disease duration and QTc, iCEB, iCEBc, and CRP levels in the Behçet group. Disease duration, age, and CRP levels were subsequently entered into a multivariable linear regression model to identify independent predictors of iCEBc. Results: Patients with BD exhibited significantly higher QTc (431.41 ± 24.18 vs. 410.60 ± 18.65 ms, p < 0.001), iCEB (4.49 ± 0.33 vs. 4.36 ± 0.35, p = 0.016), and iCEBc (5.20 ± 0.47 vs. 4.93 ± 0.33, p < 0.001) values compared with healthy controls. CRP levels were also significantly elevated in the Behçet group (23.34 ± 34.05 vs. 6.07 ± 4.57 mg/L, p < 0.001). Disease duration demonstrated significant positive correlations with QTc (r = 0.539, p < 0.001), iCEB (r = 0.447, p < 0.001), iCEBc (r = 0.747, p < 0.001), and CRP levels (r = 0.419, p < 0.001). In multivariable linear regression analysis, disease duration emerged as the sole independent predictor of iCEBc (β = 0.077, 95% CI: 0.057–0.097, p < 0.001), whereas CRP was not independently associated with iCEBc (p = 0.286). Conclusions: Patients with BD have significantly increased iCEB and iCEBc values, indicating impaired cardiac electrophysiological balance. The strong association between disease duration and iCEBc suggests a relationship between disease duration and electrophysiological alterations associated with ventricular arrhythmia risk. iCEBc may serve as a simple and noninvasive electrocardiographic marker of electrophysiological alterations associated with ventricular arrhythmia risk in patients with BD.

Background/Objectives: Cardiovascular diseases remain a leading cause of death worldwide, and early diagnosis is essential. Phonocardiogram (PCG) signals provide a non-invasive and inexpensive means of cardiac assessment. However, accurate interpretation of PCG signals requires clinical expertise. In this paper, we propose TriFusion-PCGNet, a hybrid multi-branch deep learning framework for automated heart sound classification. Methods: The proposed method was evaluated on the PhysioNet/Computing in Cardiology Challenge 2016 dataset. Three complementary PCG representations—the raw signal, band-pass filtered signal, and Hilbert envelope signal—were processed through dedicated feature extraction branches using residual convolutional networks, dilated residual convolutions, and a BiGRU with a temporal attention mechanism. The extracted features were fused and classified using fully connected layers. Performance was evaluated using the original train–validation split and stratified 10-fold cross-validation. Results: Under a strictly separated record-level 10-fold cross-validation protocol, TriFusion-PCGNet achieved 90.87% mean accuracy and 95.34% AUC, with outer test folds isolated from training and model selection. Subject-level analysis showed substantially lower performance, emphasizing the importance of subject-disjoint evaluation for patient-level generalization. Conclusions: TriFusion-PCGNet integrates complementary PCG representations for automated normal/abnormal heart sound classification. However, the substantially lower subject-disjoint performance indicates that the record-level results should not be interpreted as evidence of patient-independent generalization, and further validation on independent subject-annotated datasets is required.

Early identification of anatomically significant coronary artery disease (CAD) remains a major clinical challenge despite advances in cardiovascular diagnostics. Novel circulating biomarkers may improve risk stratification and diagnostic discrimination beyond conventional parameters. We investigated the diagnostic utility of four emerging biomarkers—Phoenixin-14, Syntenin-1, Alamandine, and Cerebellin-1—for the assessment of CAD severity. In this prospective observational study, 90 participants undergoing coronary angiography were categorized into three groups: severe CAD (≥70% stenosis; n = 30), non-obstructive/non-critical CAD (<70% stenosis; n = 30), and angiographically normal controls (n = 30). Patients with acute coronary syndrome, diabetes mellitus, prior coronary revascularization, cardiomyopathy, or significant systemic disease were excluded. Circulating biomarker concentrations were quantified using the enzyme-linked immunosorbent assay. Comparative analyses, correlation testing, and receiver operating characteristic (ROC) analyses were performed to evaluate discriminatory performance. Circulating Phoenixin-14 concentrations progressively declined across the control, non-critical CAD, and severe CAD groups [40.1 (29.0–49.7) vs. 24.4 (18.5–30.1) vs. 16.7 (13.4–19.0) pg/mL, respectively; p < 0.001]. Phoenixin-14 demonstrated outstanding discrimination for severe CAD, achieving an area under the ROC curve (AUC) of 0.969 (95% CI, 0.888–0.997), with 86.7% sensitivity and 96.7% specificity at a threshold of ≤20.2 pg/mL. Diagnostic performance was substantially lower for Syntenin-1 (AUC, 0.795), Alamandine (AUC, 0.661), and Cerebellin-1 (AUC, 0.597). Phoenixin-14 also showed robust discrimination for non-critical CAD (AUC, 0.832). Biomarker concentrations exhibited correlations with metabolic indices while remaining largely independent of traditional cardiovascular risk factors. Among the evaluated novel circulating biomarkers, Phoenixin-14 demonstrated superior diagnostic performance for both obstructive and non-obstructive CAD, markedly outperforming Syntenin-1, Alamandine, and Cerebellin-1. These findings identify Phoenixin-14 as a promising candidate biomarker for CAD severity assessment and clinical risk stratification. Larger multicenter studies are warranted to validate these exploratory findings and determine their incremental value in contemporary cardiovascular practice.

Background: Pharmacological cardioversion (PC) with antiarrhythmic agents is a common initial rhythm control strategy in patients with new-onset atrial fibrillation (AF). However, predictive tools for estimating the likelihood of successful PC remain limited. The systemic immune-inflammation index (SII), a novel composite marker derived from neutrophil, lymphocyte, and platelet counts, may reflect atrial inflammatory burden and structural remodeling. This study aimed to investigate the prognostic value of SII in predicting pharmacological cardioversion success in patients with acute-onset symptomatic AF. Methods: This prospective observational study included patients with hemodynamically stable, new-onset symptomatic AF admitted since October 2025. All patients received intravenous amiodarone for pharmacological cardioversion. Baseline clinical, echocardiographic, and laboratory parameters were recorded. Patients were classified into cardioversion-success and non-response groups based on ECG-confirmed restoration of sinus rhythm. Logistic regression analyses were performed to identify independent predictors of rhythm control, and ROC curves were generated to determine predictive performance. Results: Among 95 patients (mean age 54.2 ± 9.8 years, 48.4% female), successful pharmacological cardioversion was achieved in 74.7%. Compared to the non-response group, the cardioversion-success group had significantly lower SII levels (p < 0.001) and left atrial volume index (LAVI, p < 0.001). Multivariate analysis identified both SII and LAVI as independent predictors of cardioversion success. Inverse correlations were observed between both SII (r = −0.419, p < 0.01) and LAVI (r = −0.567, p < 0.01) and rhythm control. The optimal SII cutoff of 645.16 predicted successful rhythm restoration with 75% sensitivity and 75% specificity (AUC: 0.803, 95% CI: 0.710–0.895). Conclusions: Higher SII levels were independently associated with lower rates of successful pharmacological cardioversion in patients with new-onset atrial fibrillation. Incorporating SII into routine assessment may enhance clinical decision-making and patient stratification for rhythm control strategies.