Ahmet Ferhat Kaya

Hepatic fibrosis indices have emerged as potential prognostic markers in cardiovascular disease. This observational study included 760 consecutive patients with acute coronary syndrome (STEMI, NSTEMI, and unstable angina pectoris) admitted between March 2020 and May 2024 to evaluate the distribution of APRI, FIB-4, FIB-5, and FIB-6 across ACS subtypes and their predictive value for in-hospital mortality and cardiogenic shock. Fibrosis indices were calculated from admission laboratory values, and multivariate logistic regression and ROC curve analyses were performed. STEMI patients had significantly higher APRI (0.95±1.43 vs. 0.41±0.71 vs. 0.20±0.11), FIB-4 (3.68±4.03 vs. 1.98±2.17 vs. 1.17±0.52), and FIB-5 (5.16±1.08 vs. 4.66±0.84 vs. 4.38±0.43) than NSTEMI and unstable angina patients (all p<0.001). In-hospital mortality and cardiogenic shock occurred in 3.8% and 3.2% of patients, respectively. Cardiogenic shock, reduced left ventricular ejection fraction, and lower serum albumin independently predicted mortality, whereas older age independently predicted cardiogenic shock. None of the hepatic fibrosis indices independently predicted either outcome, and their discriminative performance was limited (AUC 0.526–0.632 for mortality and 0.570–0.662 for cardiogenic shock). These findings indicate that although hepatic fibrosis scores are elevated in STEMI, they primarily reflect the severity of acute myocardial injury rather than providing incremental prognostic value beyond established clinical predictors in patients with acute coronary syndrome.

Cardiovascular diseases (CVDs) remain among the leading causes of morbidity and mortality worldwide and represent a major public health concern [1]. In the pathogenesis of atherosclerosis, both dys...

Background: Benidipine is a long-acting calcium channel blocker used for the treatment of hypertension and chronic coronary syndrome. Although it effectively lowers blood pressure (BP), the relationship between BP reduction and improvement in angina-related health status during benidipine therapy remains uncertain. This study evaluated changes in BP and patient-reported angina-related health status in a large real-world cohort. Methods: This prospective, multicenter, observational cohort study included 1659 patients with hypertension and/or chronic coronary syndrome who underwent baseline clinical assessment before initiation of benidipine therapy and were reassessed after 3 months. Changes in systolic and diastolic BP and Seattle Angina Questionnaire-7 (SAQ-7) scores were evaluated. Multivariable linear regression analyses were performed to identify predictors of BP reduction and symptomatic improvement. A propensity score-matched analysis according to coronary artery disease (CAD) status was conducted to further explore differences in treatment response. Results: The mean age was 62.2 ± 12.2 years, and 54.0% of participants were women. Systolic BP decreased by 13.8 mmHg and diastolic BP by 6.7 mmHg (both p < 0.001). Mean SAQ-7 score increased by 8.1 points (p < 0.001), exceeding the established threshold for clinically meaningful improvement. Baseline SAQ-7 score showed the strongest association with change in SAQ-7, whereas baseline systolic BP was the strongest predictor of BP reduction. The correlation between changes in systolic BP and SAQ-7 was weak (r = 0.058, p = 0.023). In the propensity score-matched cohort (n = 732), BP reduction was similar regardless of CAD status, whereas improvement in SAQ-7 was greater in patients without obstructive CAD. Conclusions: In this multicenter real-world cohort, benidipine therapy was associated with significant BP reduction and clinically meaningful improvement in angina-related health status over 3 months. Symptomatic improvement showed only a weak association with BP reduction. These findings should be interpreted within the limitations of an observational study and warrant confirmation in controlled comparative studies.

Mechanical heart valve replacement necessitates long-term warfarin anticoagulation to prevent thromboembolic complications, yet achieving adequate anticoagulation control remains challenging. This study evaluated whether mean platelet volume (MPV) could serve as a biomarker for anticoagulation quality in mechanical heart valve recipients. We conducted a retrospective longitudinal cohort analysis of 370 patients with mechanical heart valves receiving warfarin therapy. MPV and international normalized ratio (INR) were measured at 5 time points. Time in therapeutic range (TTR) was calculated using the Rosendaal method, with effective anticoagulation defined as TTR ≥60%. Correlations between MPV, INR, and TTR were analyzed overall and stratified by valve type. Only 34.1% of patients achieved effective TTR. Mean MPV was 10.31 ± 1.04 fL and mean INR was 2.79 ± 0.54. No significant correlation was identified between MPV and TTR. However, patients with mean INR ≤3.0 exhibited significantly higher MPV values than those with INR >3.0 (10.40 vs. 10.13 fL). Subgroup analysis revealed a significant inverse correlation between MPV and INR in patients with mitral valve replacement but not in patients with aortic valve replacement, with temporal variation across measurement periods. MPV showed poor discriminatory ability for predicting effective TTR. Multivariable regression identified atrial fibrillation, older age, higher platelet counts, and higher hemoglobin as independent predictors of effective TTR, while MPV showed no independent association. In conclusion, MPV does not serve as a reliable biomarker for anticoagulation quality in patients with mechanical heart valve. Enhanced anticoagulation management strategies are needed to improve TTR in this high-risk population.

Background: Anthracycline-based chemotherapy is highly effective in breast cancer treatment but is limited by dose-dependent cardiotoxicity. Early identification of subclinical myocardial injury is crucial to prevent progression to irreversible dysfunction. Objectives: To evaluate whether a multimodal surveillance strategy integrating left ventricular global longitudinal strain (LVGLS), left atrial reservoir strain (LASr), and high-sensitivity troponin I (hs-TnI) can predict early anthracycline-induced cardiotoxicity. Methods: This retrospective cohort study included 50 female breast cancer patients (mean age 49.3 ± 8.5 years) treated between January 2022 and December 2024. Echocardiography and biomarkers were assessed at baseline and 1 month after chemotherapy. Cardiotoxicity was defined as a > 10% reduction in LVEF to < 53%. Results: Cardiotoxicity occurred in 15 patients (30%). LVGLS, LASr, and hs-TnI significantly changed (all P < 0.001). Independent predictors were LVGLS (aOR 1.33), LASr (aOR 0.77), and hs-TnI (aOR 1.07). hs-TnI showed the highest discriminative ability (AUC 0.940). Conclusions: LVGLS, LASr, and hs-TnI provide complementary information for early detection of cardiotoxicity. Graphical

Objective: This study aimed to evaluate the prognostic value of the hemoglobin–albumin–lymphocyte–platelet (HALP) score in patients with rheumatic mitral stenosis (RMS). Methods: A total of 169 patients with RMS were retrospectively analyzed over a median follow‐up period of 124 months (IQR: 96–148). The primary endpoint was major adverse cardiac and cerebrovascular events (MACCE). The predictive performance of the HALP score was assessed using receiver operating characteristic (ROC) analysis, Cox regression, and Kaplan–Meier survival analysis. Results: The HALP score demonstrated a modest but statistically significant ability to predict MACCE (AUC = 0.644, p = 0.037). The optimal cut‐off value was 35.65, with a sensitivity of 60.4% and specificity of 38.1%. Patients with lower HALP scores had a significantly higher incidence of MACCE (log‐rank p = 0.015). However, in multivariable analysis, the HALP score was not identified as an independent predictor ( p = 0.076). Conclusion: The HALP score may serve as a simple and cost‐effective complementary tool for risk stratification in patients with RMS. However, its prognostic value appears limited when used alone, and it should be interpreted alongside established clinical and echocardiographic parameters.

Background: Pregnancy induces profound cardiovascular adaptations that may have cumulative effects with repeated pregnancies. However, the relationship between parity and subclinical myocardial dysfunction in healthy women remains unclear. Methods: This prospective cross‐sectional study enrolled 605 healthy pregnant women without cardiovascular disease or risk factors. All participants underwent comprehensive echocardiographic assessment including speckle‐tracking‐derived global longitudinal strain (GLS) analysis during the third trimester (28–32 weeks gestation). The relationship between parity and GLS was described using multivariable linear regression analysis, adjusting for age, body mass index and blood pressure. Participants were stratified into two groups (≥ 4 vs. < 4 pregnancies) for comparative analysis. Results: Mean age was 30.9 ± 7.2 years with mean parity of 3.8 ± 2.4 pregnancies. Despite preserved ejection fraction across all women (63.2% ± 4.7%), each additional pregnancy was associated with a 0.21‐unit worsening in GLS independent of age ( β = 0.21, 95% CI: 0.13–0.29, p < 0.001). Women with ≥ 4 pregnancies had significantly worse GLS compared to those with fewer pregnancies (−19.1% ± 2.5% vs. −20.5% ± 4.1%, p < 0.001). In multivariable linear regression, high parity remained independently associated with reduced GLS ( β = 1.18, 95% CI: 0.52–1.84, p < 0.001) after adjusting for confounders. The effect was more pronounced in older women and those with shorter interpregnancy intervals (< 18 months). Conclusion: Higher parity is independently associated with reduced myocardial strain detected by GLS despite preserved ejection fraction, demonstrating a continuous relationship with each additional pregnancy. While GLS values remain within physiological ranges for pregnancy, the shift toward lower values suggests cumulative haemodynamic effects that warrant further investigation.

Objective: This study compared the performance of different machine learning models in predicting in-hospital mortality in patients hospitalized for heart failure. Methods: Demographic and clinical data of 229 patients were retrospectively reviewed for this study. The variables included age, sex, hypertension, diabetes, coronary artery disease, peripheral artery disease, chronic kidney failure, Charlson comorbidity index, and length of hospital stay. In-hospital mortality (“in-hospital death”) was defined as the dependent variable. The machine learning methods used included logistic regression, random forest, gradient boosting, and multilayer perceptron models. The model performance was evaluated using the receiver operating characteristic area under the curve, precision–recall area under the curve, F1 score, and Brier score. Results: In-hospital mortality was observed in 7 of the 229 patients included in the study (3.1%). The mean age and frequency of chronic kidney disease were higher among deceased patients, whereas the Charlson comorbidity index did not differ significantly between survivors and non-survivors. In model comparisons, the Gradient Boosting model showed the best overall performance (ROC-AUC: 0.87, PR-AUC: 0.76, F1: 0.73; Brier score: 0.12). Conclusion: The Gradient Boosting model demonstrated the highest performance in predicting in-hospital mortality in patients with heart failure. Machine learning algorithms offer strong potential beyond traditional statistical approaches for the prognostic prediction of complex clinical scenarios such as heart failure.