Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
Background/Objectives: Chronological age (CA) is commonly used in clinical decisionmaking, yet it may not accurately reflect biological aging. Recent advances in artificial intelligence (AI) allow estimation of electrocardiogram (ECG)-derived heart age, which may serve as a non-invasive biomarker for physiological aging. This study aimed to develop and validate a deep learning model to predict ECG-heart age in individuals with no structural heart disease. Methods: We trained a convolutional neural network (DenseNet-121) using 12-lead ECGs from 292,484 individuals (mean age: 51.4 ± 13.8 years; 42.3% male) without significant cardiac disease. Exclusion criteria included missing age data, age <18 or >90 years, and structural abnormalities. CA was used as the target variable. Model performance was evaluated using the coefficient of determination (R2), Pearson correlation coefficient (PCC), mean absolute error (MAE), and root mean square error (RMSE). External validation was conducted using 1191 independent ECGs. Results: The model demonstrated strong predictive performance (R2 = 0.783, PCC = 0.885, MAE = 5.023 years, RMSE = 6.389 years). ECG-heart age tended to be overestimated in younger adults (≤30 years) and underestimated in older adults (≥70 years). External validation showed consistent performance (R2 = 0.703, PCC = 0.846, MAE = 5.582 years, RMSE = 7.316 years). Conclusions: The proposed AI-based model accurately estimates ECG-heart age in individuals with structurally normal hearts. ECG-derived heart age may serve as a reliable biomarker of biological aging and support future risk stratification strategies....
Obstructive coronary artery disease (CAD) is common in patients undergoing transcatheter aortic valve implantation (TAVI). While invasive coronary angiography (ICA) is the gold standard for coronary evaluation, coronary computed tomography angiography (cCTA) is gaining interest for its potential to exclude obstructive CAD during pre-procedural imaging. This study aimed to assess the diagnostic accuracy of cCTA in ruling out significant CAD in TAVI candidates. We retrospectively analyzed 95 TAVI candidates (mean age 77.7 ± 8.5 years) who underwent both cCTA and ICA. Diagnostic performance of cCTA—sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy—was assessed using ICA as the reference, in both patient- and vesselbased models. Obstructive CAD was defined as ≥50% luminal stenosis or occlusion of a stent/bypass graft. ICA detected obstructive CAD in 27 patients (28.4%). Excluding non-evaluable cases, cCTA showed a negative predictive value (NPV) of 97% (patient-level) and 95% (vessel-level), with a diagnostic accuracy of 85% and 87%, respectively. Including all patients, regardless of scan quality, the NPV remained high (97%), although overall accuracy dropped to 67% (patient-level) and 66% (vessel-level). cCTA demonstrated high accuracy in excluding significant CAD, with a stable NPV of 95–97%. The relatively high rate of non-diagnostic scans and the single-center, retrospective design suggest that its role should be considered complementary to ICA, potentially reducing—but not replacing—the need for ICA in selected TAVI candidates....
Surgery and anesthesia induce a stress response that provokes increased sympathetic stimulation, secretion of cortisol, hypercoagulability, and systemic inflammatory response. All these homeostatic deteriorations, especially systemic inflammation, represent a risk for organ damage. Perioperative cardiac complications have an increasing impact on morbidity and mortality, not only in cardiovascular but also in non-cardiac surgery. Surgical procedures represent a potential trigger for systemic inflammation that causes secretion of proinflammatory cytokines, activation of neutrophils, and tissue damage. Also, increased levels of preoperative inflammatory markers predict perioperative cardiovascular events. Systemic inflammatory biomarkers increase during the first days after surgical procedures and decline within a few weeks. Besides contemporary traditional biomarkers (CRP, BNP), newer biomarkers, such as galectin-3, TNF-α, and various MiRNAs, can predict inflammatory response and related cardiac injury. Determination of inflammatory markers in the perioperative period could help identify patients at risk for cardiovascular events. The reduction in perioperative inflammatory response may improve surgical outcomes. Prevention and treatment of systemic inflammation can be achieved by optimization of surgical procedures, anesthetic regimen, and pharmacological agents, especially interleukin inhibitors. Determination of inflammatory biomarkers, along with prevention and treatment of inflammation, can improve perioperative cardiac risk reduction strategies....
Cardiovascular diseases (CVDs) cover various pathologies including heart failure (HF). Furthermore, vitamin D is involved in the regulation of the cardiovascular system. This study aimed to assess the association between the vitamin D receptor (VDR) genotypes and the occurrence of cardiovascular disorders in the Algerian population. VDR gene polymorphisms were identified using the PCR-RFLP method. Moreover, plasma concentrations of 25-hydroxyvitamin-D were assessed by a chemiluminescent immunoassay method and plasma NT-proBNP levels were determined in vitro by immunoenzymatic analysis. Interestingly, our results indicate that the genotypic frequencies of ApaI polymorphism of the VDR gene were significantly higher in CVD patients compared to the control group. Moreover, higher numbers of AA genotypes and A alleles were found in the CVD group. Our data indicate that the group of CVD patients with HF compared to those without HF showed the same genotype and allele distribution. Furthermore, low vitamin D rates and high N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels according to the VDR rs7975232 genotype were noted in CVD patients compared to healthy controls. Our results indicate that ApaI polymorphism of the VDR gene and lower vitamin D level may be associated with increased cardiovascular risk. These findings indicate that the ApaI AA genotype could be considered as a new HF risk marker in the Algerian population....
Introduction: The increasingly active role of nurses in the management of heart failure (HF) has become important in HF units (HCUs). This study aims to determine the effect of opening a specialised HF nursing (NSHF) consultation in a tertiary hospital on drug titration, and its subsequent impact on cardiac remodelling and prognosis. Methods: A retrospective cohort study was conducted on patients with HF with reduced ejection fraction (HFrEF) who were treated between 2017 and 2020. Patients who were followed by the NSHF were compared with those who underwent conventional clinical follow-up (non- NSHF), focusing on drug optimisation, echocardiographic parameters, biomarkers, and clinical outcomes in terms of mortality and hospital readmissions for HF. Results: A total of 411 patients were analysed, 85 of whom (20.7%) were treated with NSHF. There were hardly any differences in baseline characteristics. At the end of follow-up, the NSHF group had a higher prescription rate of angiotensin receptor–neprilysin inhibitor (+31.7% vs. +23.3%; p < 0.001), beta-blockers (+2.4% vs. −5.8%; p < 0.001), and sodium glucose co-transporter type 2 inhibitors (+24.7% vs. +17.8%; p < 0.001). There was also a higher rate of loop diuretic withdrawal (−16.7% vs. −6.7%; p < 0.001). However, no improvement in reverse remodelling or neurohormonal response was observed. Patients treated with NSHF had a lower probability of dying from HF (88.6% vs. 63.3%; p = 0.006), but this did not reduce hospital admissions for HF. Conclusions: Patients with HFrEF who are cared for through NSHF are more likely to be prescribed drugs that modify the prognosis of the disease. This has an impact on their mortality....
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