Background and Objectives: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease that affects approximately 1 in 500 people. Due to an incomplete disease penetrance associated with numerous factors, HCM is not manifested in all carriers of genetic mutation. Although about two-thirds of patients are male, it seems that female gender is associated with more severe disease phenotype and worse prognosis. The objective of this study was to evaluate the gender related differences in HCM presentation. Materials and Methods: This study was conducted as a part of the international multidisciplinary SILICOFCM project. Clinical information, laboratory analyses, electrocardiography, echocardiography, and genetic testing data were collected for 362 HCM patients from four clinical centers (Florence, Newcastle, Novi Sad, and Regensburg). There were 33% female patients, and 67% male patients. Results: Female patients were older than males (64.5 vs. 53.5 years, p < 0.0005). The male predominance was present across all age groups until the age of 70, when gender distribution became comparable. Females had higher number of symptomatic individuals then males (69% vs. 52%, p = 0.003), most frequently complaining of dyspnea (50% vs. 30%), followed by chest pain (30% vs. 17%), fatigue (26% vs. 13%), palpitations (22% vs. 13%), and syncope (13% vs. 8%). The most common rhythm disorder was atrial fibrillation which was present in a similar number of females and males (19% vs. 13%, p = 0.218). Levels of N-terminal pro-brain natriuretic peptide were comparable between the genders (571 vs. 794 ng/L, p = 0.244). Echocardiography showed similar thickness of interventricular septum (18 vs. 16 mm, p = 0.121) and posterolateral wall (13 vs. 12 mm, p = 0.656), however, females had a lower number of systolic anterior motion (8% vs. 16%, p = 0.020) and other mitral valve abnormalities. Conclusions: Female patients are underrepresented but seem to have a more pronounced clinical presentation of HCM. Therefore, establishing gender specific diagnostic criteria for HCM should be considered.

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.

Aims: This study aimed to assess nursing workload in Cardiac Intensive Care Unit (CICU) after three cardiothoracic surgery procedures during first four postoperative days using Nursing Activities Score (NAS) and Nine Equivalents of Nursing Manpower Use Score (NEMS) systems, to compare their performance for that purpose and to investigate association between nursing workload and type of surgery.Design: A comparative study.Methods: The research environment includes CICU of the University Hospital for Cardiovascular Diseases in Serbia. A total of 808 patients who underwent coronary, valvular, or combined surgery, resulting in 2282 filled NAS and NEMS pairs chart. Statistical analysis was performed using SPSS‐19. The correlation between NAS and NEMS was tested by Spearman's correlation coefficient. Differences were considered statistically significant at p < 0.05.Results: The lowest median of cumulative NAS 176 (175‐257) and NEMS 76 (64‐91) had coronary surgery patients, the highest NAS 224.5 (178‐334.5) and NEMS 83 (69‐121) had those with combined surgery; this difference was statistically significant (p < 0.001). The median of both scores decreased after surgery, with the following values from the first to the fourth postoperative day: NAS from 104 (102‐105) to 81 (74‐85) and NEMS from 46 (42–46) to 30 (30–37). The difference in mean values of both scores between the first and the fourth postoperative day was statistically significant (p < 0.001). NAS and NEMS were in a positive, strong correlation (r = 0.913; p < 0.005).Conclusion: Both scores can be used to measure nursing workload, identify the required number of nurses in CICU, and support task allocation. NAS may have an advantage because it better describes extensive postoperative monitoring and care needed for cardiac surgery patients. Nursing workload is associated with type of surgery, with the highest workload measured in patients who underwent combined surgery procedure and on the first postoperative day.

Predicting the long-term expansion and remodeling of the left ventricle in patients is challenging task but it has the potential to be clinically very useful. In our study, we present machine learning models based on random forests, gradient boosting, and neural networks, used to track cardiac hypertrophy. We collected data from multiple patients, and then the model was trained using the patient's medical history and present level of cardiac health. We also demonstrate a physical-based model, using the finite element procedure to simulate the development of cardiac hypertrophy. Our models were used to forecast the evolution of hypertrophy over six years. The machine learning model and finite element model provided similar results. The finite element model is much slower, but it's more accurate compared to the machine learning model since it's based on physical laws guiding the hypertrophy process. On the other hand, the machine learning model is fast but the results can be less trustworthy in some cases. Both of our models, enable us to monitor the development of the disease. Because of its speed machine learning model is more likely to be used in clinical practice. Further improvements to our machine learning model could be achieved by collecting data from finite element simulations, adding them to the dataset, and retraining the model. This can result in a fast and more accurate model combining the advantages of physical-based and machine learning modeling.

Background/Objectives: Hypertrophic cardiomyopathy (HCM) is a common heart disorder characterized by the thickening of the heart muscle, particularly in the left ventricle, which increases the risk of cardiac complications. This study aims to analyze the expression of apoptosis-regulating genes (CASP8, CASP9, CASP3, BAX, and BCL2) in blood samples from HCM patients, to better understand their potential as biomarkers for disease progression. Methods: Quantitative real-time PCR (qPCR) was used to evaluate gene expression in blood samples from 93 HCM patients. The correlation between apoptosis-regulating genes was conducted and clinical parameters were integrated for feature importance and clustering analysis. Results: Most patients exhibited significant downregulation of CASP8, CASP9, and CASP3. In contrast, BAX expression was elevated in 71 out of 93 patients, while BCL2 was increased in 55 out of 93 patients. Correlation analysis revealed weak negative correlations between the BAX/BCL2 ratio and CASP gene expression. Conclusions: These findings suggest that reduced expression of apoptotic genes may indicate a protective cellular mechanism, which could serve as a biomarker for disease progression. Further studies are needed to investigate the potential for therapeutic modulation of these pathways to improve patient outcomes.