Search Results (468)

Background: Cardiac tumors are rare neoplasms with a wide spectrum of clinical presentations, ranging from asymptomatic cases to fatal outcomes. According to the 2021 thoracic tumor classification of the World Health Organization (WHO), papillary fibroelastoma (PFE) is the most common primary cardiac tumor. This study aimed to aggregate and examine data regarding the prevalence, clinical characteristics, and histological results of cardiac tumors. Methods: This multicenter retrospective study was conducted across seven tertiary care institutions and included 274 patients diagnosed with histopathologically confirmed cardiac tumors between January 2013 and December 2024. Results: This study included 274 patients, with an average age of 52.6 ± 16.6 years. Of the study participants, 120 (43.8%) were male and 154 (56.2%) were female. The most prevalent clinical manifestations were dyspnea (43.7%), thoracic pain (22.5%), and cardiac palpitations (21.1%). Echocardiography was the principal diagnostic method, revealing an average tumor size of 3 cm. The most commonly observed mass was cardiac myxoma (CM) in 192 patients (70.1%). The second most frequently detected mass was PFE (28 cases, 10.2%). The third most common cardiac mass was a metastatic tumor (6.9%). Surgical resection was performed in all patients, with infection being the most prevalent consequence, followed by effusion. Conclusions: Cardiac tumors, albeit uncommon, provide considerable diagnostic and treatment difficulties. Our research is founded on an extensive case series that has been histopathologically validated and sourced from various national tertiary centers. This comprehensive dataset offers epidemiological and clinical insights regarding heart tumors in Turkey. Another key finding of our study is that, even though the 5th edition of the 2021 WHO Classification of Thoracic Tumors lists PFE as the most common primary cardiac tumor, myxoma is actually the most common primary cardiac tumor in our study and in many other studies. This finding demonstrates a significant discrepancy between the current international classification and real-world data and suggests that tumor distribution may be related to regional and demographic differences. Full article

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy. Full article

Cardiac fibrosis following myocardial infarction plays a critical role in the formation of scar tissue and contributes to ventricular arrhythmias, including ventricular tachycardia and sudden cardiac death. Current clinical diagnostics use electrical and structural markers, but lack precision due to low spatial resolution and absence of molecular information. In this paper, we employed line scan Raman microspectroscopy to classify sheep myocardial tissue into muscle, necrotic, granulated, and fibrotic tissue types, using collagen as a molecular biomarker. Three spectral regions were evaluated: region A (600–2960 cm⁻¹), region B (600–1399 cm⁻¹ and 1751–2960 cm⁻¹), and region C (1400–1750 cm⁻¹), which includes the prominent collagen-associated peaks at 1448 cm⁻¹ and 1652 cm⁻¹. Linear and nonlinear principal component analysis (PCA) and support vector machines (SVMs) were applied for dimensionality reduction and classification, with nonlinear models specifically addressing the nonlinearity of collagen formation during fibrogenesis. Histological validation was performed using Masson’s trichrome staining. Raman bands associated with collagen in region C consistently outperformed regions A and B, achieving the highest explained variance and best class separation in both binary and multiclass PCA models for both linear and nonlinear approaches. The ratio of collagen-related peaks enabled stage-dependent tissue characterization, confirming the nonlinear nature of fibrotic remodeling. Our findings highlight the diagnostic potential of collagen-associated Raman bands for characterizing myocardial fibrosis. The proposed PCA-SVM framework demonstrates robust performance even with limited sample size and has the potential to lay the foundation for real-time intraoperative diagnostics. Full article

Although platelets have been traditionally thought of to be essential hemostasis mediators, new research shows how important they are for controlling cellular oxidative stress, inflammatory processes, and immunological responses—particularly during major surgery on the abdomen. Perioperative problems are largely caused by the continually changing interaction of inflammatory cytokines, the formation of reactive oxygen species (ROS), and platelet activation. The purpose of this review is to summarize the most recent data regarding the complex function of platelets in abdominal surgery, with an emphasis on how they interact with inflammation and oxidative stress, and to investigate the impact on postoperative therapy and subsequent studies. Recent study data on platelet biology, redox signals, surgical stress, and antiplatelet tactics was reviewed in a systematic manner. Novel tailored therapies, perioperative antiplatelet medication, oxidative biomarkers of interest, and platelet-derived microscopic particles are important themes. In surgical procedures, oxidative stress dramatically increases the reactive capacity of platelets, spurring thromboinflammatory processes that affect cardiac attacks, infection risk, and recovery. A number of biomarkers, including soluble CD40L, thromboxane B2, and sNOX2-derived peptide, showed potential in forecasting results and tailored treatment. Antiplatelet medications are still essential for controlling risk factors for cardiovascular disease, yet using them during surgery necessitates carefully weighing the risks of thrombosis and bleeding. Biomarker-guided therapies, antioxidant adjuncts, and specific platelet inhibitors are examples of evolving tactics. In abdominal procedures, platelets strategically operate at the nexus of oxidative stress, inflammatory processes, and clotting. Improved patient classification, fewer problems, and the creation of individualized surgical care strategies could result from an increased incorporation of platelet-focused tests and therapies into perioperative processes. To improve clinical recommendations, subsequent studies may want to focus on randomized studies, biomarker verification, and using translational approaches. Full article

Background: Preparing patients for surgery considers assessing the patient’s somatic health, for example by the American Society of Anesthesiology (ASA) scale or the Revised Cardiac Risk Index (RCRI), known as the Lee index. This process usually ignores mental functioning (personality and anxiety), which is known to influence health. The purpose of this study is to analyze the existence of a relationship between personality traits (the Big Five model and trait-anxiety) and anesthesia scales (ASA scale, Lee index) used for the preoperative evaluation of patients. Methods: The study group comprised 102 patients (59 women, 43 men) scheduled for hip replacement surgery. Patients completed two psychological questionnaires: the NEO-FFI (NEO Five Factors Inventory) and the X-2 STAI (State-Trait Anxiety Inventory) sheet. Next, the presence and possible strength of the relationship between personality traits and demographic and medical variables were analyzed using Spearman’s rho rank correlation coefficient. Results: Patients with a high severity of trait anxiety are classified higher on the ASA scale (rs = 0.359; p < 0.001). Neuroticism, defined according to the Big Five model, significantly correlates with scales of preoperative patient assessment: the ASA classification (rs = 0.264; p < 0.001) and the Lee index (rs = 0.202; p = 0.044). A hierarchical regression model was created to test the possibility of predicting ASA scores based on personality. It explained more than 34% of the variance and was a good fit to the data (p < 0.05). The controlled variables of age and gender accounted for more than 23% of the variance. Personality indicators (trait anxiety, neuroticism) additionally accounted for slightly more than 11% of the variance. Trait anxiety (Beta = 0.293) proved to be a better predictor than neuroticism (Beta = 0.054). Conclusions: These results indicate that inclusion of personality screening in the preoperative patient evaluation might help to introduce a more individualized approach to patients, which could result in better surgical outcomes. Full article

Background and Objectives: Cardiac transplantation is currently the elective treatment choice in end-stage heart failure, and cellular rejection is a predictive factor for morbidity and mortality after surgery. We proposed an evaluation of the clinicopathologic factors involved in the mechanism of rejection. Materials and Methods: This study included 146 patients who underwent transplantation at the Institute of Cardiovascular Diseases and Transplantation in Targu Mures between 2010 and 2023, and we evaluated the function and structure of the myocardium after surgery by using endomyocardial biopsy. Results: Overall, 120 men and 26 women underwent transplantation, with an approximately equal proportion under and over 40 years old (48.6% and 51.4%). Evaluating the degree of acute cellular rejection according to the International Society for Heart and Lung Transplantation classification showed that most of the patients presented with acute cellular rejection (ACR) and antibody-mediated rejection (AMR) grade 0, and most cases of ACR and AMR were reported with mild changes (13% or 10.3% patients). Therefore, the most frequent histopathologic diagnoses were similar to lesions unrelated to rejection (45.2% of patients) and ischemia–reperfusion lesions (25.3% patients), respectively. Conclusions: Although 82.2% of the transplanted cases showed no rejection (ISHLT score 0), non-rejection-related lesion-like changes were present in 45.2% of cases, and because more of the non-rejection-related criteria could be detected, it may be necessary to adjust the grading of the rejection criteria. The histopathologic changes that characterize rejection are primarily represented by the mononuclear inflammatory infiltrate; in our study, inflammatory changes were mostly mild (71.9%), with myocyte involvement in all cases. These changes are associated with and contribute to the maintenance of the rejection phenomenon. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

In clinical practice, left ventricle hypertrophy (LVH) continues to pose a considerable challenge, highlighting the need for more reliable diagnostic approaches. This study aims to propose an automated framework for the quantification of LVH extent and the classification of myocardial segments according to hypertrophy severity using a deep learning-based algorithm. The proposed method was validated on 133 subjects, including both healthy individuals and patients with LVH. The process starts with automatic LV segmentation using U-Net and the segmentation of the left ventricle cavity based on the American Heart Association (AHA) standards, followed by the division of each segment into three equal sub-segments. Then, an automated quantification of regional wall thickness (RWT) was performed. Finally, a convolutional neural network (CNN) was developed to classify each myocardial sub-segment according to hypertrophy severity. The proposed approach demonstrates strong performance in contour segmentation, achieving a Dice Similarity Coefficient (DSC) of 98.47% and a Hausdorff Distance (HD) of 6.345 ± 3.5 mm. For thickness quantification, it reaches a minimal mean absolute error (MAE) of 1.01 ± 1.16. Regarding segment classification, it achieves competitive performance metrics compared to state-of-the-art methods with an accuracy of 98.19%, a precision of 98.27%, a recall of 99.13%, and an F1-score of 98.7%. The obtained results confirm the high performance of the proposed method and highlight its clinical utility in accurately assessing and classifying cardiac hypertrophy. This approach provides valuable insights that can guide clinical decision-making and improve patient management strategies. Full article

There is a lack of studies on the effectiveness of polar-transformed spectrograms in the visualization and prediction of cardiac arrhythmias from electrocardiogram (ECG) data. In this study, single-lead ECG waveforms were converted into two-dimensional rectangular time–frequency spectrograms and polar time–frequency spectrograms. Three pre-trained convolutional neural network (CNN) models (ResNet50, MobileNet, and DenseNet121) served as baseline networks for model development and testing. Prediction performance and visualization quality were evaluated across various image resolutions. The trade-offs between image resolution and model capacity were quantitatively analyzed. Polar-transformed spectrograms demonstrated superior delineation of R-R intervals at lower image resolutions (e.g., 96 × 96 pixels) compared to conventional spectrograms. For deep-learning-based classification of cardiac arrhythmias, polar-transformed spectrograms achieved comparable accuracy to conventional spectrograms across all evaluated resolutions. The results suggest that polar-transformed spectrograms are particularly advantageous for deep CNN predictions at lower resolutions, making them suitable for edge computing applications where the reduced use of computing resources, such as memory and power consumption, is desirable. Full article

Heart failure (HF) is a complex clinical syndrome that results from any structural or functional impairment of ventricular filling or ejection of blood. The etiology of heart failure is multifactorial, encompassing ischemic heart disease, hypertension, valvular disorders, cardiomyopathies, and metabolic and infiltrative diseases. Despite advances in pharmacologic and device-based therapies, heart failure continues to carry a substantial burden of morbidity, mortality, and healthcare utilization. With the advancement and increased accessibility of cardiac imaging modalities, the diagnostic accuracy for identifying the underlying etiologies of nonischemic cardiomyopathy has significantly improved, allowing for more precise classification and tailored management strategies. This review aims to provide a comprehensive analysis of the current understanding of heart failure, encompassing epidemiology, etiological factors, with a specific focus on diagnostic imaging modalities including the role of echocardiography and strain imaging, cardiac magnetic resonance imaging (CMR), cardiac computed tomography (CT), and nuclear positron emission tomography (PET) imaging and recent advances in the diagnosis and management of heart failure. Full article

Background: This study aimed to develop a predictive model to assess risk factors and prognoses in pediatric patients with dilated cardiomyopathy (DCM). Methods: A total of 233 pediatric patients with DCM who were hospitalized between January 2019 and June 2024 were enrolled. The children were followed up and categorized into two groups: the death/heart transplantation (D/HT) group and the non-D/HT group. Univariate and multivariate analyses identified risk factors. A nomogram model and a scoring system were developed. The performance of these models was evaluated using the H-L test, ROC analysis, and internal validation. Results: The results demonstrated that the age of onset, cardiac functional classification III–IV, moderate-to-severe mitral regurgitation, low voltage in limb leads on an ECG, and the need for vasoactive drugs are independent predictors of D/HT risk in children with DCM. A nomogram model was developed, achieving an AUC of 0.804 (95% CI: 0.734–0.874), a sensitivity of 80.3%, and a specificity of 66.7%. A scoring system was established: 1 point for age of onset, 10 points for cardiac functional classification III–IV, 2.5 points for moderate-to-severe mitral regurgitation, 4 points for low voltage in limb leads on an ECG, 3 points for the need for vasoactive drugs, or 0 points if none of these criteria were met. When the cumulative score was ≥ 13.25, the sensitivity and specificity increased to 68.9% and 73.9%, respectively. Conclusions: We developed both a nomogram and a scoring system model, which are capable of rapidly and accurately predicting the risk of D/HT in children with DCM. Full article

Background: ESC sports classification in 2020, based on cardiac morphological adaptations, may not fully reflect also the variations in functional parameters of athletes. This study aims to characterize CPET-derived physiological parameters in elite athletes according to the ESC classification and evaluate whether this morphological classification also corresponds to a functional categorization. Methods: Elite athletes underwent pre-participation screening before the 2023 European Games and 2024 Olympic Games. Athletes were classified into four categories (skill, power, mixed and endurance). CPET was performed on a cycle ergometer using a ramp protocol, with measurements of VO₂ max, heart rate, power output and ventilatory efficiency. Results: We enrolled 1033 athletes (46.8% females; mean 25.6 ± 5.2 years old) engaged in skill (14.1%), power (33.2%), mixed (33.3%) and endurance (19.4%) disciplines. O₂ pulse showed an incremental significant increase (p < 0.0001) among sport categories (skill 14.9 ± 3.8 mL/beat; power 17.5 ± 4.6 mL/beat, mixed 19 ± 4.3 mL/beat and endurance 22.7 ± 5.8 mL/beat). The lowest ${\dot{\mathrm{V}}\mathrm{O}}_2$max was observed in skill disciplines (36.3 ± 7.9 mL/min/kg) whilst endurance ones showed the highest values (52.4 ± 9.7 mL/min/kg) (p < 0.0001). ${\dot{\mathrm{V}}\mathrm{O}}_2$max was higher in power compared to mixed (42 ± 7.7 mL/min/kg vs. 40.5 ± 5.8 mL/min/kg, p = 0.005) disciplines with an overlapping amount between some mixed and power disciplines. No differences were found for VE max (p = 0.075). Conclusions: Our study provided values of CPET parameters in elite athletes. Significant differences in CPET parameters were observed among different sports disciplines, with endurance athletes showing the highest absolute and relative values in all parameters. An overlap amount was noted between mixed and power categories, especially for relative maximal oxygen consumption. Full article

Background/Objectives: Long QT Syndrome type 2 (LQT2) is a cardiac channelopathy linked to pathogenic variants in the KCNH2 gene, which encodes the Kv11.1 potassium channel, essential for cardiac repolarization. Variants affecting splice sites disrupt potassium ion flow, prolong QT interval, and increase the risk of arrhythmias and sudden cardiac death (SCD). Understanding genotype–phenotype correlations is key, given the variability of clinical manifestations even within families sharing the same variant. We aimed to evaluate new pathogenic variants by analyzing genotype–phenotype correlations in informative families. Methods: Genetic and clinical assessments were performed on index cases and family members carrying KCNH2 pathogenic variants, referred for genetic testing between 2010 and June 2023. The next-generation sequencing (NGS) of 210 cardiovascular-related genes was conducted. Clinical data, including demographic details, family history, arrhythmic events, electrocardiographic parameters, and treatments, were collected. Results: Among 390 patients (152 probands) tested for LQTS, only 2 KCNH2 variants had over 5 carriers. The detailed clinical information of 22 carriers of this KCNH2 p.Ser261fs. has already been reported by our research group. Moreover, we identified 12 carriers of the KCNH2 c.77-2del variant, predicted to disrupt a splice site and not previously reported. Segregation analysis showed its high penetrance, supporting its classification as pathogenic. Conclusions: The newly identified KCNH2 c.77-2del variant is a pathogenic, as strongly supported by the segregation analysis. Our findings underscore the importance of further research into splice site variants to enhance clinical management and genetic counseling for affected families. Full article

Background: In the emergency setting, many diagnostic pathways incorporate change in high-sensitivity cardiac troponin (hs-cTn) concentrations (i.e., the delta) to classify patients as low-risk (rule-out) or high-risk (rule-in) for possible myocardial infarction (MI). However, the impact of analytical variation on the delta for correct classification is unknown, especially at concentrations below and around the 99th percentile. Our objective was to assess the impact of delta variation for correct risk classification across the European Society of Cardiology (ESC 0/1 h and 0/2 h), the High-STEACS, and the common change criteria (3C) pathways. Methods: A yearlong accuracy study for hs-cTnT was performed where laboratories across Canada tested three patient-based samples (level 1 target value = 6 ng/L, level 2 target value = 9 ng/L, level 3 target value = 12 ng/L) monthly across 41 different analyzers. The assigned low-delta between levels 1 and 2 was 3 ng/L (i.e., 9 − 6 = 3 ng/L) and the assigned high-delta between levels 1 and 3 was 6 ng/L (i.e., 12 − 6 = 6 ng/L). The low- and high-deltas for each analyzer were determined monthly from the measured values, with the difference calculated from the assigned deltas. The obtained deltas were then assessed via the different pathways on correct classification (i.e., percent correct with 95% confidence intervals, CI) and using non-parametric analyses. Results: The median (interquartile range) difference between the measured versus assigned low-delta (n = 436) and high-delta (n = 439) was −1 ng/L (−1 to 0). The correct classification differed among the pathways. The ESC 0/1 h pathway yielded the lowest percentage of correct classification at 35.3% (95% CI: 30.8 to 40.0) for the low-delta and 90.0% (95% CI: 86.8 to 92.6) for the high-delta. The 3C and ESC 0/2 h pathways yielded higher and equivalent estimates on correct classification: 95.2% (95% CI: 92.7 to 97.0) for the low-delta and 98.2% (95% CI: 96.4 to 99.2) for the high-delta. The High-STEACS pathway yielded 99.5% (95% CI: 98.4 to 99.9) of correct classifications for the high-delta but only 36.2% (95% CI: 31.7 to 40.9) for the low-delta. Conclusions: Analytical variation will impact risk classification for MI when using hs-cTn deltas alone per the pathways. The 3C and ESC 0/2 h pathways have <5% misclassification when using deltas for hs-cTnT in this dataset. Additional studies with different hs-cTnI assays at concentrations below and near the 99th percentile are warranted to confirm these findings. Full article

Objective: We aimed to investigate the association between the pan-immune-inflammation value (PIV) and mortality in patients with heart failure with a reduced ejection fraction (HFrEF), along with clinical and biochemical parameters. Methods: In this retrospective cohort study, 419 patients diagnosed with HFrEF between January 2014 and December 2023 were analyzed. Data on demographic features, comorbidities, cardiac parameters [New York Heart Association (NYHA) classification, left ventricular ejection fraction (LVEF), ventricular dimensions], medication use, and laboratory findings (PIV, N-terminal pro-B-type natriuretic peptide [NT-proBNP], electrolytes, and complete blood count) were collected from institutional and national records. Results: Mortality occurred in 22.91% of patients. PIV > 696 was significantly associated with mortality (sensitivity: 37.5%, specificity: 78.64%, p = 0.006), but it was not an independent predictor in multivariate analysis. Instead, low body mass index (BMI), increased end-systolic diameter, reduced LVEF, advanced NYHA class (III/IV), elevated NT-proBNP, hyponatremia, and lymphopenia were identified as independent predictors (all p < 0.001). Conclusions: Although PIV was associated with mortality in patients with HFrEF, it did not independently predict outcomes beyond established risk factors. These results suggest that while inflammation may contribute to HFrEF pathophysiology, traditional clinical and biochemical markers remain more reliable for prognostication. Full article