Izolowana bąblowica serca - opis przypadku

Cardiac hydatidosis is rarely encountered. A case of isolated cardiac hydatid cyst of the left ventricle in a young woman is described. The diagnosis was suggested by echocardiography and was confirmed later by pathological evaluation. Serological tests were negative. The patient underwent a successful operation for cyst resection

Left Atrial Malignant Fibrous Histiocytoma with Right Atrium Invasion

Background: Primary cardiac tumors are rare (0.001 to 0.03). Malignant tumors account for 25, of which 75 are cardiac sarcomas. Case Persentation: Here, we report a case of a 57-year-old male with palpitation and history of left atrial (LA) myxoma resection presented to cardiology clinic for postsurgical follow up and transthoracic echocardiography revealed a large non-homogenous mass in LA with right atrium invasion, which was confirmed by trans-esophageal echocardiography. The patient underwent surgical resection of tumor and the pathological diagnosis was malignant fibrous histiocytoma (MFH). Conclusion: MFH could be asymptomatic and the diagnosis be established as a surgical or complementary examination. In patients with history of myxoma resection and cardiac masses, further evaluation is recommended. &#160

Association of Significant Mitral Regurgitation and Left Ventricular Dysfunction With ALCAPA Syndrome in a Young Patient

Anomalous origin of the left coronary artery from pulmonary artery (ALCAPA) is not a common anomaly in adulthood. Its early diagnosis requires physician suspicion and the early treatment of disease can prevent its serious side effects. In this article, we presented a young female with pansystolic murmur and heart failure with final diagnosis of ALCAPA syndrome

Prevalence of Mitral Valve Disease in Pregnancy and its Effects on Maternal-Fetal Outcomes

Objective: Heart diseases are among the most prevalent diseases that endanger the life of both mother and fetus and cause complications for them. Rheumatic mitral valve is the most prevalent organic involvement in pregnant women with mitral valve disease in Southeast Asia. Mitral valve disease is a serious and common problem for pregnant women. Despite medical and surgical advances in treatment of heart diseases, it is still the fourth cause of mortality in pregnant women. Accordingly, the purpose of this study was to assess the prevalence of mitral valve disease in pregnancy and its effects on maternal-fetal outcomes. Material and Methods: This cross-sectional study was conducted on pregnant women with heart problems who were selected through convenience sampling. Data were analyzed in SPSS version 17.0. Results: Findings showed that heart valve problems were the most frequent medical history of pregnant women. Among the subjects, the most prevalent heart disease was related to Mitral Stenosis (MS) (39.6%) and mitral valve prolapse (MVP) (22.8%). The most frequent causes of hospitalization were high blood pressure (43.2%) and chest pain (38.2%). The mean age of participants was 25±83 years. Conclusion: Heart diseases during pregnancy are highly risky, but their progress and complications for mother and fetus can be avoided by constant prevention and treatment before and during pregnancy

Mitral Regurgitation after Percutaneous Balloon Mitral Valvotomy in Patients with Rheumatic Mitral Stenosis: A Single-Center Study

Background: Percutaneous balloon mitral valvotomy (BMV) is the gold standard treatment for rheumatic mitral stenosis (MS) in that it causes significant changes in mitral valve area (MVA) and improves leaflet mobility. Development of or increase in mitral regurgitation (MR) is common after BMV. This study evaluated MR severity and its changes after BMV in Iranian patients. Methods: We prospectively evaluated consecutive patients with severe rheumatic MS undergoing BMV using the Inoue balloon technique between February 2010 and January 2013 in Madani Heart Center, Tabriz, Iran. New York Heart Association (NYHA) functional class and echocardiographic and catheterization data, including MVA, mitral valve mean and peak gradient (MVPG and MVMG), left atrial (LA) pressure, pulmonary artery systolic pressure (PAPs), and MR severity before and after BMV, were evaluated. Results: Totally, 105 patients (80% female) at a mean age of 45.81 ± 13.37 years were enrolled. NYHA class was significantly improved after BMV: 55.2% of the patients were in NYHA functional class III before BMV compared to 36.2% after the procedure (p value < 0.001). MVA significantly increased (mean area = 0.64 ± 0.29 cm2 before BMV vs. 1.90 ± 0.22 cm2 after BMV; p value < 0.001) and PAPs, LA pressure, MVPG, and MVMG significantly decreased. MR severity did not change in 82 (78.1%) patients, but it increased in 18 (17.1%) and decreased in 5 (4.8%) patients. Patients with increased MR had a significantly higher calcification score (2.03 ± 0.53 vs.1.50 ± 0.51; p value < 0.001) and lower MVA before BMV (0.81± 0.23 vs.0.94 ± 0.18; p value = 0.010). There were no major complications. Conclusion: In our study, BMV had excellent immediate hemodynamic and clinical results inasmuch as MR severity increased only in some patients and, interestingly, decreased in a few. Our results, underscore BMV efficacy in severe MS. The echocardiographic calcification score was useful for identifying patients likely to have MR development or MR increase after BMV

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation

Precision Diagnostics in Cardiac Tumours:Integrating Echocardiography and Pathology with Advanced Machine Learning on Limited Data

This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, echo malignancy, and echo position, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94% in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation