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    Do current indices predict cardioversion success in patients with atrial fibrillation? A retrospective observational study
    (Walter de Gruyter GmbH, 2024) Sefa Tatar; Yunus Emre Yavuz; Emirhan Feyzullahoglu; Ahmet Lütfi Sertdemir; Abdullah Icli; Hakan Akilli
    Objective: Atrial fibrillation (AF) is one of the leading arrhythmias that causes serious complications. Our aim is to investigate the factors predicting the success of cardioversion in patients who underwent the procedure due to AF. Methods: A total of 107 patients who underwent cardioversion were included in the study. Patients were divided into groups based on cardioversion success. Demographic, echocardiographic, and laboratory characteristics were compared between the groups. Results: Hypertension and diabetes mellitus were more frequent in patients with successful cardioversion, but no statistically significant difference was found between the groups (p > 0.05). The pre-procedure leukoglycemic index (LGI) was found to be higher in the successful cardioversion group. However, this difference was not statistically significant between the groups (p > 0.05). Although the fibrosis-4 (FIB-4) index and systemic immune-inflammation index (SII) were numerically higher in the group with unsuccessful cardioversion, no statistically significant difference was observed between the groups (p > 0.05). Echocardiographic parameters such as left atrial diameter and mitral regurgitation rate were higher in patients with successful cardioversion, but no significant difference was detected between the groups (p > 0.05). Conclusion: AF is a significant arrhythmia that may lead to high mortality and morbidity. Various scoring systems have been developed to predict cardioversion success. The LGI, FIB-4 index, and SII are potential predictors of cardioversion success. However, these parameters alone are insufficient to predict cardioversion success. Further large-scale randomized studies are needed to clarify the effectiveness of these parameters.
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    Machine Learning and Shock Indices-Derived Score for Predicting Contrast-Induced Nephropathy in ACS Patients
    (Ovid Technologies (Wolters Kluwer Health), 2025-02-13) Yunus Emre Yavuz; Sefa Tatar; Hakan Akıllı; Muzaffer Aslan; Abdullah İçli
    Background Contrast-induced nephropathy (CIN) is a serious complication following acute coronary syndrome (ACS), leading to increased morbidity and mortality. Machine learning (ML), combined with parameters such as shock indices, can potentially improve CIN risk prediction by analyzing complex variable interactions and creating accessible, clinically applicable models. Methods This retrospective case-control study included 719 ACS patients who underwent percutaneous coronary intervention (PCI). Patients were divided into two groups (CIN and non-CIN), and clinical, procedural, and hemodynamic parameters, including shock indices, were analyzed using machine learning algorithms. A new predictive model, CIN-Predict 5, was developed using the Gradient Boosting Machine (GBM) algorithm, incorporating clinically relevant and statistically significant variables. Correlations between model predictions and secondary outcomes, including in-hospital mortality and hospitalization duration, were evaluated. Results Among the variables used in the GBM algorithm, the Modified Shock Index emerged as the most significant predictor, with an importance score of 0.25. The CIN-Predict 5 model achieved an AUC of 0.87, outperforming the Mehran Risk Score (AUC = 0.75) for predicting CIN. The secondary outcomes showed that CIN-Predict 5 correlated significantly with in hospital mortality (r = 0.16, p < 0.001) and hospitalization duration (r = 0.20, p < 0.001). Conclusions The GBM-based model we developed, utilizing shock indices and derived through ML, provides a practical tool for early identification of high-risk CIN patients post-ACS, enabling timely preventive strategies and improving clinical decision-making. © 2025 Wolters Kluwer Health, Inc. All rights reserved.