Ufuk İyIgün

Background: Coronary artery calcification (CAC) is a critical marker of subclinical atherosclerosis, yet its primary diagnostic tool, coronary computed tomography (CT), is limited by cost and radiation. We aimed to develop and validate a robust machine learning framework to identify significant CAC (Agatston score ≥100) using features extracted from routine 12-lead electrocardiograms (ECGs), including legacy archives stored as PDFs. Methods: This retrospective study utilized 382 consecutive patient records. A novel signal-processing pipeline was developed to convert PDF-based ECGs into standardized 500-Hz digital waveforms (WFDB format). From these signals, 360 morphological and temporal features were extracted using the NeuroKit2 library. To ensure clinical reliability and prevent optimistic bias, a Balanced Random Forest (BRF) classifier was integrated into a repeated “inner-outer” nested cross-validation framework combined with Minimum Redundancy Maximum Relevance (mRMR) feature selection. Results: The mRMR-BRF model demonstrated high diagnostic accuracy with a grand mean AUC of 0.8211 (95% CI: 0.8018–0.8404), outperforming standard algorithms. Explainability analysis via SHAP identified age, ST-segment level in lead V4, T/QRS ratio in lead III, and QRS duration dispersion as the most significant predictors. These features suggest that latent electrical signatures of subclinical myocardial remodeling and impaired perfusion are detectable via AI-enhanced ECG analysis. Conclusion: Our framework establishes digitized 12-lead ECGs as a scalable and non-invasive digital biomarker for significant CAC. By enabling the quantitative analysis of legacy PDF archives and providing high predictive stability through nested validation, this approach offers a cost-effective pre-screening tool to prioritize high-risk patients for definitive imaging and early preventive intervention.

Purpose This study aimed to evaluate the safety and efficacy of transcatheter tricuspid valve repair using the TriClip system in patients with severe tricuspid regurgitation (TR) and large coaptation gaps (> 10 mm), a subgroup traditionally considered anatomically challenging for edge-to-edge repair. Methods A retrospective analysis was conducted on 9 symptomatic patients who underwent TriClip implantation at multiple centers in Turkey between March 2024 and May 2025. All patients had severe or greater TR with coaptation gaps exceeding 10 mm and were deemed high-risk for surgical intervention. Clinical and echocardiographic parameters were collected before and after the procedure. Results The mean age of patients was 68.4 ± 8.6 years. The average coaptation gap was 17.56 ± 3.43 mm. The procedure achieved 100% technical success, defined as at least a one-grade reduction in TR severity. The mean number of clips implanted was 3.22 ± 0.83, with the XTW clip used in 86.3% of cases. No major complications or in-hospital mortality occurred. Symptomatic improvement was observed in all patients, with most experiencing at least one New York Heart Association (NYHA) class reduction. Mean follow-up duration was 6.7 ± 2.4 months. Conclusion TriClip repair appears to be a safe and effective therapeutic option in high-risk patients with severe TR and large coaptation gaps. Despite anatomical complexity, significant reductions in TR severity and clinical symptoms were achieved, with low complication rates. These findings support the feasibility of TriClip therapy in this challenging patient population.

Electrocardiogram Changes in Patients with Hyponatremia: A Retrospective Study - International Journal of the Cardiovascular Academy