Depression and anxiety are common psychological disorders following acute coronary syndrome (ACS), potentially affecting patient outcomes. Sex-based disparities in the prevalence of these disorders may significantly impact recovery and rehabilitation post-ACS. To evaluate gender-based differences in depression and anxiety prevalence among Turkish patients following (ACS), and to identify sociodemographic and clinical factors associated with psychological distress. In this cross-sectional observational study, 604 patients (144 women and 460 men) diagnosed with ACS were enrolled at their initial outpatient follow-up (4-8 weeks post-discharge). Data collection included demographic and clinical surveys along with the Hospital Anxiety and Depression Scale (HADS). Depression and anxiety were significantly more prevalent in women than men (61.1% vs. 37.4% for depression, p < 0.001; 30.6% vs. 20.0% for anxiety, p = 0.008). Moderate-to-severe symptoms were also more common among women for both conditions (p < 0.001). Lower education levels and income below expenses were associated with higher anxiety and depression scores (p < 0.025). Among women, married individuals reported higher anxiety than widowed/divorced women (p < 0.001). Hypertension significantly increased distress, particularly in men (p < 0.001), while chronic kidney disease had a stronger impact among women (p < 0.018). There are marked gender disparities in depression and anxiety prevalence post-ACS, with women being significantly more affected. Socioeconomic disadvantages and comorbid conditions contribute to psychological distress. Routine psychological screening and sex-sensitive interventions, especially for vulnerable groups, are crucial for improving outcomes after ACS. Significant gender disparities exist in post-ACS depression and anxiety, with women disproportionately affected. Socioeconomic disadvantage and comorbidities further compound psychological distress. Integrating routine mental health screening and sex-sensitive interventions into post-ACS care is essential, particularly for high-risk populations, to improve long-term outcomes.

Background: This study aims to compare the performance of machine learning (ML) models developed to predict long-term mortality risk in patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (pPCI) and to investigate the prognostic value of novel inflammatory–metabolic indices. Methods: In this retrospective study, 329 consecutive STEMI patients who underwent pPCI (292 survivors, 37 deaths) were included. Five ML algorithms—Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Artificial Neural Networks (ANN)—were developed for mortality prediction. Model performance was evaluated using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). SHAP (Shapley Additive exPlanations) analysis was used to interpret model decision mechanisms. Results: The mortality group had significantly higher door-to-balloon time (DTBT), Systemic Inflammatory Response Index (SIRI), pan-immune-inflammation value (PIV), whereas body mass index (BMI), Prognostic Nutritional Index (PNI), and Advanced Lung Cancer Inflammation Index (ALI) values were significantly lower (p < 0.001). Among the ML models, the XGBoost algorithm achieved the best performance, with 98.99% accuracy, a ROC-AUC of 0.999, and 100% sensitivity, correctly identifying all mortality cases. SHAP analysis identified DTBT, albumin level, and ALI score as the strongest predictors of mortality, in that order. Conclusions: The XGBoost algorithm provides high accuracy and reliability for predicting long-term mortality in STEMI patients. Beyond DTBT, integrating novel indices—especially ALI and TyG—into ML models may serve as a powerful clinical tool for early identification of high-risk patients and improved risk stratification.

Background: Myocardial infarction (MI) is a disease characterised by myocardial necrosis due to acute and prolonged ischaemic hypoxia in the coronary arteries. MOTS-c is a mitochondrial- derived peptide that has been reported to have protective effects on cardiac tissue. Although this peptide is thought to be decreased in various diseases and can serve as a potential biomarker, current studies remain limited. Objective: This study aimed to evaluate how the post-treatment process affects circulating MOTS- c peptide levels in myocardial infarction patients. Methods: For this purpose, patients without obstructive coronary lesions on angiography were included in the control group, while those with significant obstructive coronary lesions on angiography were included in the infarction group. Routine biochemistry tests were performed using an autoanalyzer. Besides, serum MOTS-c levels were measured using ELISA. Results: Our findings showed CRP, ESR, and troponin I levels to be higher in the MI group compared to the control group. Also, there was no significant change in MOTS-c levels between the control and the MI group, while time-dependent changes (day 0, day 3, and day 30) occurred within the MI group. However, a negative correlation was found between MOTS-c and platelet levels in the MI group at day 0 (r: -0.4417, p =0.0450). Similarly, MOTS-c was found to be negatively correlated with troponin I in the MI group at day 3 (r: -0.4571, p =0.0372). Conclusion: The negative correlation of MOTS-c level with both platelet and troponin I has shown that this peptide may contribute to the diagnostic and therapeutic evaluation of the MI process along with other parameters.

Background/Objectives: Nonvalvular atrial fibrillation (NVAF) is a prevalent arrhythmia associated with elevated risks of stroke, systemic embolism, and mortality. Emerging evidence underscores the pivotal role of inflammation in NVAF pathogenesis. The CHA₂DS₂-VA score is currently the most powerful tool used in the management of patients with atrial fibrillation, and integrating novel inflammatory biomarkers—neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI)—into this score may enhance prognostic accuracy and guide personalized therapy. Methods: In this observational case–control study, a cohort of 330 NVAF patients and 201 controls, inflammatory and biochemical parameters were measured and compared, we employed multivariate logistic regression and ROC analyses to validate the discriminative power of novel inflammatory indexes and novel CHA₂DS₂-VA score, setting a new benchmark for biomarker integration in NVAF management. Results: Inflammatory indexes (NLR, PLR, SII, SIRI) were significantly higher in NVAF patients compared to controls (p < 0.001). Multivariate analysis identified NLR (OR = 4.02), PLR (OR = 1.04), SII (OR = 1.01), and SIRI (OR = 1.87) as independent NVAF risk markers. The CHA₂DS₂-VA score showed the strongest association with NVAF (OR = 5.55), and an optimal cutoff of ≥2 yielded 88.18% sensitivity and 74.63% specificity. Conclusions: Inflammatory markers NLR, PLR, SII, and SIRI, when assessed alongside the CHA₂DS₂-VA score, offer significant and complementary prognostic insight for patients with NVAF. These findings support the integration of inflammatory indexes into routine clinical risk assessment models to enhance early identification of high-risk individuals and inform personalized therapeutic strategies. Moreover, our findings provide a rationale for developing composite risk scores in future studies that integrate inflammatory biomarkers with the CHA₂DS₂-VA score (e.g., a CHA₂DS₂-VA-Inflammation Score). Further large-scale, longitudinal studies are warranted to validate these results and explore the benefits of inflammation-targeted interventions.

Background: Systemic inflammation through neutrophil-mediated injury, lymphocyte depletion, and monocyte-driven fibrosis plays a central pathophysiological role in heart failure (HF) progression. We investigated the diagnostic and prognostic utility of contemporary inflammatory indices, particularly the Systemic Inflammatory Response Index (SIRI) and Naples Prognostic Score (NPS). Methods: This retrospective cohort study enrolled 926 participants (500 HF patients, 426 controls). Multiple inflammatory indices (e.g., SIRI, Prognostic Nutritional Index (PNI)) and prognostic scores (e.g., NPS) were calculated from routine hematological and biochemical parameters. Primary outcomes were HF diagnosis discrimination and 3-month and 24-month all-cause mortality. Receiver operating characteristic (ROC) curve analysis, Kaplan–Meier survival curves, and Cox proportional hazards regression were performed. Results: HF patients demonstrated significantly elevated inflammatory burden: SIRI (3.26 vs. 1.06, p < 0.001) and NPS (2.00 vs. 1.43, p < 0.001). For HF diagnosis, SIRI exhibited superior discriminative performance (AUC = 0.893; 95% confidence interval (CI): 0.871–0.912), substantially exceeding all other indices (p < 0.001). For long-term mortality prediction, SIRI maintained the highest accuracy (AUC = 0.677), followed by PNI (AUC = 0.639) and NPS (AUC = 0.613). Kaplan–Meier analysis revealed progressive survival deterioration across NPS categories: 24-month mortality increased from 5.9% (NPS = 0) to 23.0% (NPS = 4), p = 0.002. Multivariable Cox regression confirmed independent prognostic value: SIRI >1.86 (HR = 2.232; 95% CI: 1.280–3.892; p = 0.005) and NPS > 2 (HR = 1.403; 95% CI: 1.180–1.668; p < 0.0001). Conclusions: SIRI and NPS represent powerful, readily accessible prognostic tools capturing distinct but complementary pathophysiological domains in HF. These indices offer substantial clinical utility for risk identification and treatment decisions, particularly in resource-limited settings. Future studies should validate these cut-offs and evaluate biomarker-guided therapeutic strategies.