Elderly patients scheduled for major elective vascular surgery are at high risk for a major adverse cardiac events (MACE). The objectives of the study were: (1) To determine the individual discriminatory ability of four risk prediction models and four biomarkers in predicting MACEs in elderly patients undergoing major elective vascular surgery; (2) to find a prognostic model with the best characteristics; (3) to examine the significance of all preoperative parameters; and (4) to determine optimal cut-off values for biomarkers with best predictor capabilities. We enrolled 144 geriatric patients, aged 69.97 ± 3.73 years, with a 2:1 male to female ratio. Essential inclusion criteria were open major vascular surgery and age >65 years. The primary outcome was the appearance of MACEs within 6 months. These were noted in 33 (22.9%) patients. The most frequent cardiac event was decompensated heart failure, which occurred in 22 patients (15.3%). New onset atrial fibrillation was registered in 13 patients (9%), and both myocardial infarction and ventricular arrhythmias occurred in eight patients each (5.5%). Excellent discriminatory ability (AUC >0.8) was observed for all biomarker combinations that included the N-terminal fragment of pro-B-type natriuretic peptide (NT-proBNP). The most predictive two-variable combination was the Geriatric-Sensitive Cardiac Risk Index (GSCRI) + NT-proBNP (AUC of 0.830 with a 95% confidence interval). Female gender, previous coronary artery disease, and NT-proBNP were three independent predictors in a multivariate model of binary logistic regression. The Cox regression multivariate model identified high-sensitivity C-reactive protein and NT-proBNP as the only two independent predictors.

Introduction. The Revised Cardiac Risk Index (RCRI) is an extensively used simple risk stratification tool advocated by the European Society of Cardiology and European Society of Anesthesiology (ESC/ESA). Purpose. The aim of this study was to find the best model for predicting 3-month cardiovascular complications in elective major vascular surgical patients using preoperative clinical assessment, calculation of the RCRI and Vascular Physiological and Operative Severity Score for the enumeration of mortality and morbidity (V-POSSUM) scores, and the preoperative levels of N-terminal brain natriuretic peptide (NT pro-BNP), high-sensitivity troponin I (hs TnI), and high-sensitivity C-reactive protein (hs CRP). Materials and Methods. We included 122 participants in a prospective, single-center, observational study. The levels of NT pro-BNP, hs CRP, and hs TnI were measured 48 hours prior to surgery. During the perioperative period and 90 days after surgery the following adverse cardiac events were recorded: myocardial infarction, arrhythmias, pulmonary edema, acute decompensated heart failure, and cardiac arrest. Results. During the first 3 months after surgery 29 participants (23.8%) had 50 cardiac complications. There was a statistically significant difference in the RCRI score between participants with and without cardiac complications. ROC analysis showed that a combination of RCRI with hs TnI has good discriminatory power (AUC 0.909, p<0,001). By adding NT pro-BNP concentrations to the RCRI+hs TnI+V-POSSSUM combination we obtained the model with the best predictive power for 3-month cardiac complications (AUC 0.963, p<0,001). Conclusion. We need to improve preoperative risk assessment in participants scheduled for major vascular surgery by combining their clinical scores with biomarkers. Therefore, it is possible to identify patients at risk of cardiovascular complications who need adequate preoperative diagnosis and treatment.

Background and Objectives: Despite significant advances in surgical techniques and perioperative care, major adverse cardiovascular events (MACE) remain a leading cause of postoperative morbidity and mortality in patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Accurate preoperative risk stratification is essential yet often limited by models that overlook atrial mechanics and underutilized biomarkers. Materials and Methods: This study aimed to develop an interpretable machine learning model for predicting perioperative MACE by integrating clinical, biochemical, and echocardiographic features, with a particular focus on novel physiological markers. A retrospective cohort of 131 patients was analyzed. An Extreme Gradient Boosting (XGBoost) classifier was trained on a comprehensive feature set, and SHapley Additive exPlanations (SHAPs) were used to quantify each variable’s contribution to model predictions. Results: In a stratified 80:20 train–test split, the model initially achieved an AUC of 1.00. Acknowledging the potential for overfitting in small datasets, additional validation was performed using 10 independent random splits and 5-fold cross-validation. These analyses yielded an average AUC of 0.846 ± 0.092 and an F1-score of 0.807 ± 0.096, supporting the model’s stability and generalizability. The most influential predictors included total atrial conduction time, mitral and tricuspid annular orifice areas, and high-density lipoprotein (HDL) cholesterol. These variables, spanning electrophysiological, structural, and metabolic domains, significantly enhanced discriminative performance, even in patients with preserved left ventricular function. The model’s transparency provides clinically intuitive insights into individual risk profiles, emphasizing the significance of non-traditional parameters in perioperative assessments. Conclusions: This study demonstrates the feasibility and potential clinical value of combining advanced echocardiographic, biochemical, and machine learning tools for individualized cardiovascular risk prediction. While promising, these findings require prospective validation in larger, multicenter cohorts before being integrated into routine clinical decision-making.

Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery, increasing morbidity and healthcare costs. This study aimed to identify echocardiographic predictors of POAF to improve risk stratification. A total of 131 patients undergoing cardiac surgery were analyzed and divided into two groups based on POAF occurrence. Echocardiographic analysis showed that patients with POAF had larger left and right atrial dimensions and impaired atrial function. Prolonged total atrial conduction time (TACT), reduced atrial emptying volumes, and contractile function were more common in the POAF group. Univariable analysis identified LAEF (χ² = 71.8, p < 0.001), LAKE (χ² = 70.1, p < 0.001), RATEF (χ² = 65.7, p < 0.001), and RAAEF (χ² = 66.8, p < 0.001) as significant predictors of POAF, each with an area under the curve (AUC) greater than 0.89. In multivariable analysis, LAKE (OR = 0.27, p < 0.001), hypertension (OR = 11.87, p = 0.035), left ventricular ejection fraction (OR = 1.08, p = 0.020), and peripheral vascular disease (OR = 40.28, p = 0.002) were independent predictors. The final model showed a significant discriminatory ability (AUC = 0.94). LAKE and clinical factors remained independent predictors after adjustment.

Introduction: The soluble urokinase-type plasminogen activator receptor (suPAR) in uPAR soluble form is produced when the uPAR is cleaved from the cell membrane during the inflammatory process. Postoperative atrial fibrillation (AF) is the most common perioperative cardiac arrhythmia. It is speculated that elevated suPAR has a role in the development of AF. The aim of our study was to investigate the predictive role of preoperative suPAR in the occurrence of AF during the first 6 months after major vascular surgery. Methods: We included 119 male and 63 female patients with an average age of 67.19 ± 6.02 years, without permanent/persistent AF. Basic predictive model (BASIC) included traditional risk factors for AF: age, gender, body mass index-BMI, smoking status, presence of arterial hypertension, diabetes mellitus and dyslipidemia. Results: Over the 6-month period, 19 (10.4%) patients had one new episode of atrial fibrillation and 2 patients (1.1%) had 2 episodes of paroxysmal atrial fibrillation. Paroxysms of AF were significantly more frequent in patients who had a resection of an abdominal aneurysm than in patients with other types of major vascular surgery. BASIC had good discriminatory ability in the prediction of AF paroxysms during the first 6 months after surgery (AUC = 0.715, 95%CI 0.590-0.840). Adding suPAR to the basic model significantly improved the discriminative ability of the predictive model for AF episodes (ΔAUC = 0.238, p < 0.001). The predictive performance of the model BASIC+CRP+suPAR, measured using AUC, NRI and IDI statistics, was very similar to the model BASIC+suPAR. Conclusion: AF is a common complication in surgical patients with high mortality and morbidity. suPAR could improve the ability of traditional risk factors to predict its occurrence up to 6 months after major vascular surgery.