Background: Early risk stratification in acute decompensated heart failure with reduced ejection fraction (ADHF-rEF) remains challenging. The Endothelial Activation and Stress Index (EASIX)—a composite of lactate dehydrogenase, creatinine, and platelet count—reflects endothelial dysfunction, a pathophysiological contributor to early deterioration in ADHF-rEF. This study evaluated the prognostic utility of admission-based EASIX for in-hospital mortality. Methods: In this retrospective single-center cohort, 850 consecutive patients hospitalized with ADHF-rEF between January 2022 and June 2025 were analyzed. EASIX was calculated from first-day laboratory values. Logistic regression, ROC analysis, restricted cubic splines, and Kaplan–Meier survival methods were used to assess the association between EASIX and in-hospital mortality, and to evaluate its incremental value beyond established clinical and laboratory predictors. Results: In-hospital mortality was 12.4%. Higher EASIX values were significantly associated with mortality in both univariable and multivariable models (adjusted OR 1.273; p < 0.001). EASIX demonstrated moderate discriminative performance among evaluated biomarkers (AUC 0.751) and showed a clear dose–response risk gradient, with mortality rising from 1.4% in the lowest tertile to 26.2% in the highest. Incorporating EASIX into clinical and laboratory prediction models yielded substantial continuous net reclassification improvement (0.59 and 0.38, respectively). Survival curves diverged early and remained distinctly separated across EASIX strata. Conclusions: Admission EASIX is an independent predictor of in-hospital mortality in ADHF-rEF and provides complementary prognostic information beyond conventional models. This is the first study to demonstrate the prognostic value of EASIX in the ADHF-rEF setting, supporting its potential utility as an accessible endothelial stress biomarker for early risk stratification.

Background: Early risk stratification in ST-elevation myocardial infarction (STEMI) remains critical, particularly for anticipating adverse outcomes such as the coronary no-reflow phenomenon (NRP) and early mortality. The Endothelial Activation and Stress Index (EASIX), calculated from routine laboratory parameters, has emerged as a potential biomarker reflecting systemic endothelial dysfunction. This study evaluated the prognostic value of admission EASIX for the NRP and in-hospital mortality in STEMI patients undergoing primary percutaneous coronary intervention (pPCI). Methods: In this retrospective single-center cohort, 1931 STEMI patients treated with pPCI between January 2023 and January 2025 were included. EASIX was calculated at admission. NRP was defined as post-PCI TIMI flow ≤ 2 or TIMI 3 flow with impaired myocardial blush (TMPG ≤ 1). Multivariable logistic regression, reclassification analyses (NRI/IDI), ROC analysis, and calibration methods were used to assess predictive performance. Sensitivity and interaction analyses were conducted. Results: NRP occurred in 14.1%, and in-hospital mortality was 2.5%. EASIX was independently associated with both outcomes (NRP: adjusted OR 1.485, 95% CI 1.286–1.715; mortality: adjusted OR range 1.371–2.096 across models; all p < 0.001). EASIX significantly improved risk reclassification for both NRP and in-hospital mortality (NRI > 0.20). ROC-AUCs were 0.706 for NRP and 0.810 for mortality. Restricted cubic spline and LOWESS analyses revealed nonlinear risk escalation. Calibration plots and Brier scores confirmed model reliability. Associations persisted across ischemic time and renal function strata. Conclusions: Admission EASIX is independently associated with NRP and in-hospital mortality in STEMI. Easily accessible and integrative, EASIX may enhance early risk stratification. External validation is warranted before clinical implementation.

Background and Objectives: Conventional risk models may not adequately capture key biological determinants of mortality following transcatheter aortic valve implantation (TAVI), such as inflammation, malnutrition, and immune dysfunction. The Osaka Prognostic Score (OPS), incorporating CRP, albumin, and lymphocyte count, may address this gap. We aimed to evaluate the prognostic value of OPS for predicting in-hospital and one-year all-cause mortality after TAVI. Materials and Methods: In this retrospective single-center cohort study, 244 patients who underwent transfemoral TAVI between December 2022 and January 2024 were analyzed. OPS was calculated at baseline (range: 0–3), and its prognostic value for in-hospital and one-year all-cause mortality was evaluated using multivariable Cox regression, ROC analysis, and restricted cubic spline (RCS) modeling. Results: In-hospital and one-year mortality rates were 11.5% and 21.7%, respectively. Higher OPS scores were significantly associated with mortality in both periods. OPS independently predicted in-hospital mortality (HR: 2.018; 95% CI: 1.632–2.521; p = 0.017) and one-year mortality (HR: 2.125; 95% CI: 1.300–3.473; p = 0.003). ROC analysis yielded AUC values of 0.776 and 0.859 for in-hospital and one-year mortality, respectively. Kaplan–Meier curves revealed significantly reduced survival in patients with higher OPS (log-rank p < 0.001), and RCS analysis demonstrated a significant nonlinear association between increasing OPS values and mortality risk (p < 0.001). Conclusions: OPS is a simple, cost-effective, and biologically relevant prognostic index that independently predicts both in-hospital and one-year mortality following TAVI. By integrating markers of inflammation, nutrition, and immune competence, OPS may offer additional value in risk stratification and support clinical decision-making in this high-risk population.

Resumo Fundamento O paradoxo do fumante tem sido motivo de debate para pacientes com infarto agudo do miocárdio (IM) há mais de duas décadas. Embora haja muitas evidências demonstrando que não existe tal paradoxo, publicações defendendo desfechos melhores em fumantes pós-IM ainda são lançadas. Objetivo Explorar o efeito do fumo na mortalidade de longo prazo após infarto do miocárdio por elevação de ST (STEMI). Métodos Este estudo incluiu pacientes com STEMI que foram diagnosticados entre 2004 e 2006 em três [...] Resumo Fundamento O paradoxo do fumante tem sido motivo de debate para pacientes com infarto agudo do miocárdio (IM) há mais de duas décadas. Embora haja muitas evidências demonstrando que não existe tal paradoxo, publicações defendendo desfechos melhores em fumantes pós-IM ainda são lançadas. Objetivo Explorar o efeito do fumo na mortalidade de longo prazo após infarto do miocárdio por elevação de ST (STEMI). Métodos Este estudo incluiu pacientes com STEMI que foram diagnosticados entre 2004 e 2006 em três

Background and Objectives: Contrast-associated acute kidney injury (CA-AKI) is a frequent and clinically significant complication in patients with acute myocardial infarction (AMI) undergoing coronary angiography. Early and accurate risk stratification remains challenging with conventional models that rely on linear assumptions and limited variable integration. This study aimed to evaluate and compare the predictive performance of multiple machine learning (ML) algorithms with traditional logistic regression and the Mehran risk score for CA-AKI prediction and to explore key determinants of risk using explainable artificial intelligence methods. Materials and Methods: This retrospective, single-center study included 1741 patients with AMI who underwent coronary angiography. CA-AKI was defined according to KDIGO criteria. Multiple ML models, including gradient boosting machine (GBM), random forest (RF), XGBoost, support vector machine, elastic net, and standard logistic regression were developed using routinely available clinical and laboratory variables. A weighted ensemble model combining the best-performing algorithms was constructed. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model interpretability was evaluated using feature importance and SHapley Additive exPlanations (SHAP). Results: CA-AKI occurred in 356 patients (20.4%). In multivariable logistic regression, lower left ventricular ejection fraction, higher contrast volume, lower sodium, lower hemoglobin, and higher neutrophil-to-lymphocyte ratio (NLR) were independently associated with CA-AKI. Among ML approaches, the weighted ensemble model demonstrated the highest discriminative performance (AUC 0.721), outperforming logistic regression and the Mehran risk score (AUC 0.608). Importantly, the ensemble model achieved a consistently high NPV (0.942), enabling reliable identification of low-risk patients. Explainability analyses revealed that inflammatory markers, particularly NLR, along with sodium, uric acid, baseline renal indices, and contrast burden, were the most influential predictors across models. Conclusions: In patients with AMI undergoing coronary angiography, interpretable ML models, especially ensemble and gradient boosting-based approaches, provide superior risk stratification for CA-AKI compared with conventional methods. The high negative predictive value highlights their clinical utility in safely identifying low-risk patients and supporting individualized, risk-adapted preventive strategies.

Contrast-induced nephropathy (CIN) is a serious complication following percutaneous coronary intervention (PCI). The Renal dysfunction, Congestive heart failure, Hypertension, Age ≥75 years, Diabetes mellitus and prior Stroke R2CHADS2 score, originally developed for thromboembolic risk assessment, has recently been proposed as a predictor of CIN and major adverse cardiovascular events (MACE). The present study aimed to evaluate the predictive value of the R2CHADS2 score for CIN and long-term MACE in STEMI patients undergoing primary PCI (pPCI). A total of 1204 STEMI patients were included in this retrospective study. Patients were categorized into 3 risk groups based on their R2CHADS2 scores. CIN incidence was significantly higher in the high-risk group (37.1%) compared with the moderate (13.6%) and low-risk groups (5.5%; P < .001). Hemodialysis was required in 14.3% of high-risk patients ( P < .001). MACE occurred in 49.3% of the high-risk group, 22.1% of the moderate-risk group, and 12.9% of the low-risk group ( P < .001). These findings suggest that the R2CHADS2 score can predict CIN and adverse cardiovascular outcomes in STEMI patients undergoing pPCI.