Browsing by Author "Ayhan, Gorkem"

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    Effect of Side Branch Predilatation Before Provisional Stenting in Coronary Bifurcation Lesions
    (Bayrakol Medical Publisher, 2025) Kanal, Yucel; Ayhan, Gorkem; Koc, Ulku Nur
    Aim: Percutaneous coronary intervention (PCI) for bifurcation lesions remains technically challenging. Despite the development of single-stent provisional techniques and various dual-stent strategies, the problem of side branch (SB) occlusion and restenosis remains unresolved. In our study, we aimed to compare the side branch (SB) patency at the end of the procedure in patients with bifurcation lesions and significant SB disease, undergoing provisional stenting, with and without SB predilatation. Materials and Methods: This retrospective observational study included 115 patients who underwent provisional PCI for true bifurcation lesions between January 2021 and November 2024. Patients were divided into two groups: those who received SB predilatation before provisional stenting and those who did not.
    Results: The mean age of the 115 patients in our study was 64.6 +/- 10.4 years, and 31% were female. In the predilatation group, SB lesion severity, procedure duration, dissection rate, and kissing balloon requirement were significantly higher compared to the non-predilatation group (p < 0.05). Discussion: Our study showed that routine SB predilatation during provisional stenting of true bifurcation lesions is associated with higher rates of dissection and kissing balloon requirement. Similar to the literature, routine SB predilatation does not appear to be recommended. The use of NC balloons in predilatation may be beneficial, as it may reduce dissection rates. Large-scale studies with a high number of patients will be helpful in further elucidating these findings.
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    Deep Learning Improves the MAGGIC Risk Score in Predicting Contrast-Induced Nephropathy in ST Elevation Myocardial Infraction Patients
    (Sage Publications Inc, 2025) Sarikaya, Remzi; Saylik, Faysal; Kumet, Omer; Ayhan, Gorkem; Kaya, Ahmet Ferhat; Can, Veysi; Kalenderoglu, Koray
    Contrast-induced nephropathy (CIN) is a serious complication in ST-elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (pPCI). Early identification of high-risk patients is essential to improve outcomes and reduce mortality. The Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score was originally designed to predict mortality in heart failure patients, but its role in predicting CIN has not been fully explored. In the present retrospective study, 1403 STEMI patients treated with pPCI were analyzed. Those who developed CIN had higher mortality, longer hospital stays, and more comorbidities. The MAGGIC score and 21 clinical parameters were incorporated into deep learning (DL) models, including multilayer perceptrons, TabNet, TabTransformer, and Kolmogorov-Arnold Networks (KAN) and one machine learning algorithm such as logistic regression. The best-performing model, KAN, significantly improved CIN prediction with an area under curve (AUC) of 0.92. SHapley Additive exPlanations (SHAP) analysis revealed key predictors such as pain-to-balloon time, contrast volume, baseline creatinine, and MAGGIC score. Our findings demonstrate that combining MAGGIC risk scoring with DL substantially enhances CIN prediction in STEMI patients. This approach enables identification of at-risk individuals and supports implementation of nephroprotective strategies at an early stage. The web-based calculator may assist clinical decision making.