Velimir Peric

Background and Objectives: Postoperative atrial fibrillation (POAF) remains one of the most frequent complications after cardiac surgery, increasing the risk of morbidity, prolonged hospitalization, and adverse long-term outcomes. Although several clinical and echocardiographic factors have been associated with POAF, the integrated contribution of atrial conduction delay, biatrial mechanics, and atrioventricular coupling to arrhythmogenesis remains unclear. Materials and Methods: This retrospective study included 131 adult patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Preoperative echocardiography within one week before surgery provided detailed assessment of atrial phasic function, valvular motion, and total atrial conduction time (TACT). Univariate analysis was followed by multivariable modeling using penalized logistic regression (Elastic Net) to identify the most robust predictors of POAF. Discriminative performance and calibration were evaluated via receiver operating characteristic (ROC) and calibration analysis. An exploratory Extreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP) analysis was used to confirm the stability and directionality of nonlinear feature interactions. Results: POAF occurred in 47 (36%) patients. The Elastic Net model identified prolonged TACT, reduced right atrial active emptying fraction (RAAEF), increased indexed minimal left atrial volume (MIN LA/BSA), and lower tricuspid annular plane systolic excursion (TAPSE) as the most informative predictors. The model demonstrated excellent internal discrimination (AUC = 0.95; 95% CI 0.91–0.99) and satisfactory calibration (Hosmer–Lemeshow p = 0.41). Exploratory XGBoost analysis yielded concordant feature hierarchies, confirming the physiological consistency of the results. Conclusions: POAF arises from an identifiable electromechanical substrate characterized by atrial conduction delay, biatrial mechanical impairment, and reduced atrioventricular coupling. A parsimonious, regularized statistical model accurately delineated this profile, while complementary machine-learning analysis supported its internal validity. These findings underscore the potential of echocardiographic electromechanical parameters for refined preoperative risk stratification, pending prospective multicenter validation.

In the context of pharmacological intervention for pain, Transient Receptor Potential Vanilloid, member 1 (TRPV1), as a non-selective cation channel belonging to the transient receptor potential (TRP) family of ion channels, has emerged as a promising target. However, the availability of selective TRPV1 antagonists and their associated pharmacological properties remains limited. This research paper explores various QSAR modeling techniques applied to a range of piperazinyl-aryl compounds acting as TRPV1 antagonists. The descriptors utilized in the creation of conformation-independent QSAR models included local molecular graph invariants and the SMILES notation, along with the incorporation of the Monte Carlo optimization method as a model development technique. Several statistical methods were employed to evaluate the quality, robustness, and predictive capacity of the developed models, yielding positive results. For the best developed QSAR model following statistical parameters were obtained for training set R2 = 0.7155, CCC = 0.8134, IIC = 0.7430, Q2 = 0.6970, RMSE = 0.645, MAE = 0.489 and F = 157; and for test set R2 = 0.9271, CCC = 0.9469, IIC = 0.9635, Q2 = 0.9241, RMSE = 0.367, MAE = 0.329 and F = 328. Additionally, molecular fragments derived from SMILES notation descriptors, which explain observed changes in the evaluated activity, were identified, leading to the design of four new antagonists. The final validation of the QSAR model and the designed antagonists was conducted through molecular docking, which demonstrated strong correlation with the QSAR modeling results.

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.

Background and Objectives: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Despite extensive research, predicting POD remains challenging due to the multifactorial and often non-linear nature of its risk factors. This study aimed to improve POD prediction using an interpretable machine learning approach and to explore the combined effects of clinical, biochemical, and perioperative variables. Materials and Methods: This study included 131 patients who underwent CABG or AVR. POD occurrence was assessed using standard diagnostic criteria. Clinical, biochemical, and perioperative variables were collected, including patient age, sedation type, and mechanical ventilation status. Machine learning analysis was performed using an XGBoost classifier, with model interpretation achieved through SHapley Additive exPlanations (SHAP). Univariate logistic regression was applied to identify significant predictors, while SHAP analysis revealed variable interactions. Results: POD occurred in 34.3% of patients (n = 45). Patients who developed POD were significantly older (67.7 ± 6.5 vs. 64.5 ± 8.7 years, p = 0.020). Sedation with mechanical ventilation and the type of sedative used were strongly associated with POD (both p < 0.001). Sedation during mechanical ventilation showed the strongest association (OR = 2520.0; 95% CI: 80.9–78,506.7; p < 0.00001). XGBoost classifier achieved excellent performance (AUC = 0.998, accuracy = 97.6%, F1 score = 0.976). SHAP analysis identified sedation, mechanical ventilation, and their interactions with fibrinogen, troponin I, leukocyte parameters, and lung infection as key predictors. Conclusions: This study demonstrates that an interpretable machine learning approach can enhance POD prediction, providing insights into the combined impact of multiple clinical, biochemical, and perioperative factors. Integration of such models into perioperative workflows may enable early identification of high-risk patients and support individualized preventive strategies.

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.

Background and Objectives: Coagulation disorders during COVID-19 infection are associated with a poorer prognosis and higher disease severity because thrombosis and inflammation are two processes that interfere with each other. A very important issue for clinicians is timely and adequate hemostasis and inflammation monitoring to prevent and treat potentially lethal consequences. The aim of this study was to identify specific hemostatic parameters that are associated with a higher risk of intrahospital mortality. Materials and Methods: This study was approved by the Ethics Committee of the Clinical Center Nis in Serbia. One hundred and forty-two patients presented with COVID-19 ARDS and were admitted to the ICU in the Clinic for Anesthesiology at the Clinical Center Nis from 14 April 2020 to 25 May 2020. Upon admission, blood was collected for biochemical and coagulation testing. The data obtained were analyzed using the Statistical Package for Social Sciences (SPSS v. 25, Chicago, IL, USA). Results: Among all the parameters assessed, older age; increased levels of fibrinogen, INR, D-dimer, and presepsin; and higher results in the platelet aggregation tests (aggregation induced by adenosine diphosphate based on the ADP test (AU/min), aggregation induced by arachidonic acid based on the ASPI test (AU/min), and aggregation induced by thrombin based on the TRAP test (AU/min)) and some assays of the viscoelastic test (clot amplitude after 5 min in the extrinsic coagulation pathway based on the A5 EX-test (mm), clot amplitude after 10 min in the extrinsic coagulation pathway based on the A10 EX-test (mm), clot amplitude after 5 min regarding functional fibrinogen based on the A5 FIB-test (mm), clot amplitude after 10 min regarding functional fibrinogen based on the A10 FIB-test (mm), and maximum clot firmness based on the MCF FIB-test (mm)); and lower values of viscoelastic clotting time in the extrinsic coagulation pathway based on the CT EX-test (s) were significantly correlated with mortality. In the multivariate analysis, D-dimer levels above 860 ng/mL, higher TRAP test value bins, and values above the normal reference range of the A10 FIB test were found to be independent predictors of mortality. Conclusions: Sophisticated hemostasis parameters can contribute to early risk assessment, which has initially been performed only on the basis of patients’ clinical status. Hypercoagulability is the main coagulation disorder in COVID-19 infection.

Revascularization of the myocardium under conditions of extracorporeal circulation cause clinical problems related to the effects of the intervention itself on the immune system and inflammatory response, especially systemic inflammatory response (Engl. Systemic Inflammatory Response Syndrome, SIRS). This prospective study included 100 patients who underwent single, double and triple surgical revascularization of the myocardium, at the Clinic for Cardiac Surgery of CC Niš, in the period from January 15., 2020. until January 15., 2021. 22 female persons - 22.0% and 78 male persons - 78.0%) were included. Preoperatively, as well as 8 hours after the surgical intervention, the following parameters were determined for the subjects: blood count (erythrocyte count - Rbc, hemoglobin - Hgb, hematocrit - Hct, leukocyte count - Wbc, platelet count - Plt) and CRP, soluble CD14 molecule (sCD 14) - presepsin, levels of the following cytokines: TNFα, IL-1β, IL-2, IL-4, IL-6, IL-10, IL-12 p70 and IFN γ. Patients in whom the extracorporeal circulation method was applied had, postoperatively, a higher concentration of CRP (p<0.001), as well as a concentration of presepsin (p<0.05) - a greater number of patients had a concentration of presepsin above the limit of 600 pg/ mL (p<0.01). Four patients out of 51 (7.84%) in the ECB group had elevated presepsin values over 1000 pg/ml and high risk according to clinical criteria for the development of systemic bacterial infection and sepsis. No such patients were recorded in the off pump group. Also, in the group where the extracorporeal blood flow system was applied, 17.64% of patients (9/51) met the clinical criteria for SIRS with elevated presepsin values over 600 pg/mL, while in the other group of patients, 2 out of 49 (4.08%) met the clinical criteria and laboratory criteria for SIRS. Except for TNFa, the levels of IL-1β, IL-2, IL-6 and IL-10 showed an increase after the intervention in all groups compared to the preoperative period (p<0.01). However, comparing the degree of cytokine increase in the postoperative period between the control (off pump) and the extracorporeal blood flow group did not show a statistical difference (IL-2; p=0.881, IL-1; p=0.709, IL-6; p=0.911, IL- 10, p=0.179). In the postoperative period, a higher incidence rate of SIRS was noted in patients in whom the ECB system was used, which also indicates its importance in the development of systemic inflammation and immune dysregulation.

Reliance on surgery, improvements in surgical techniques, and perioperative management have led to a dramatic increase in the frequency of surgical procedures in the elderly population. According to the European Society of Cardiology and the European Society of Anaesthesiology and Intensive Care, major vascular surgery is defined as high-risk, considering the frequency of myocardial infarction and cardiac arrest, which is higher than 5%. Age is an independent predictor of myocardial infarction and cardiac arrest. Heart function alterations are primary changes that occur with advancing age. Risk stratification represents a set of procedures that include identifying chronic systemic diseases, determining their severity, stability and the need for further evaluation and/or therapy optimization, with the aim of reducing perioperative and postoperative mortality and morbidity. In addition to the application of risk scores, accurate risk stratification requires the combined application of both preoperative and postoperative biomarkers. The main idea of integrating biomarkers with scoring systems is to reveal those patients with clinically unmanifested disease, who carry a mortality risk and remain undetected by scoring systems. Biomarkers, such as NT-proBNP and highly sensitive C-reactive protein, have the greatest predictive influence in geriatric vascular surgery.

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.