Search

This comprehensive review explores the intricate aspects of left ventricular thrombus (LVT), a potential complication in both ischemic and non-ischemic cardiomyopathies. It provides a thorough understanding of left ventricular thrombus, revealing its uncommon incidence in the general population (7 cases per 10,000 patients), predominantly linked to ischemic heart diseases (ICMs) at an 80% prevalence rate. Diagnostic tools, notably transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (CMR), demonstrate varying sensitivity but remain indispensable in specific clinical contexts related to LVT as non-invasive diagnostic modalities. A detailed comparison between ICM patients and those with non-ischemic cardiomyopathy (NICM) who have left ventricular thrombus reveals subtle distinctions with significant clinical implications. This analysis underscores the importance of these imaging techniques in distinguishing between the two conditions. Additionally, we explored the occurrence of LVT in specific non-ischemic cardiomyopathies, including Takotsubo syndrome, hypertrophic cardiomyopathy, eosinophilic myocarditis, Chagas disease, cardiac amyloidosis, and several other conditions. The article further delves into anticoagulation strategies, thoroughly examining their impact on LVT regression and patient outcomes. Pharmacological interventions, with a focus on direct oral anticoagulants, emerge as promising alternatives; however, there is insufficient information on their efficiency and safety, especially in NICM population. In conclusion, this review highlights the complex nature of LVT, incorporating a range of etiopathogenic factors, diagnostic complexities, and evolving therapeutic approaches. It emphasizes the pressing need for ongoing research in this field. [ABSTRACT FROM AUTHOR]

The aim of this retrospective study was to identify myocardial injury after COVID-19 inflammation and explore whether myocardial damage could be a possible cause of the persistent symptoms following COVID-19 infection in previously healthy individuals. This study included 139 patients who were enrolled between January and June 2021, with a mean age of 46.7 ± 15.2 years, of whom 68 were men and 71 were women without known cardiac or pulmonary diseases. All patients underwent clinical work-up, laboratory analysis, cardiac ultrasound, and CMR on a 1.5 T scanner using a recommended protocol for morphological and functional assessment before and after contrast media application with multi-parametric sequences. In 39% of patients, late gadolinium enhancement (LGE) was found as a sign of myocarditis. Fibrinogen was statistically significantly higher in patients with LGE than in those without LGE (4.3 ± 0.23 vs. 3.2 ± 0.14 g/L, p < 0.05, respectively), as well as D-dimer (1.8 ± 0.3 vs. 0.8 ± 0.1 mg/L FEU). Also, troponin was statistically significantly higher in patients with myocardial LGE (13.1 ± 0.4 ng/L) compared to those with normal myocardium (4.9 ± 0.3 ng/L, p < 0.001). We demonstrated chest pain, fatigue, and elevated troponin to be independent predictors for LGE. Septal LGE was shown to be a predictor for arrhythmias. The use of CMR is a potential risk stratification tool in evaluating outcomes following COVID-19 myocarditis. [ABSTRACT FROM AUTHOR]

The purpose of the study was to identify factors characterizing a decrease in LV global systolic function in patients with ST-segment elevation myocardial infarction (STEMI) after revascularization using cardiac magnetic resonance imaging (MRI)-based ischemic injury pattern and laboratory parameters sensu left ventricular global systolic function. A total of 109 STEMI patients were examined. The patients underwent contrast-enhanced cardiac MRI with a 1.5 Tesla GE SIGNA Voyager (GE HealthCare, Chicago, IL, USA) on the 7th–10th days from the onset of the disease. According to cardiac MRI analysis, the patients were divided into the following groups with regard to left ventricular ejection fraction (LVEF) values: Group 1—patients with LVEF ≥ 50%; group 2—patients with mildly reduced LVEF 40–49%; group 3—patients with low LVEF < 40%. A predominance of most parameters of the ischemic injury pattern was noted in patients with mildly reduced and low LVEF versus patient group with LVEF ≥ 50%. Some risk factors for a decrease in LVEF < 50% systolic function in STEMI patients after revascularization were revealed: male gender; time from the onset of the anginal attack to revascularization; coronary artery status; several LV parameters; ischemic injury characteristics; natriuretic peptide and troponin I levels. [ABSTRACT FROM AUTHOR]

Ischemic heart condition is one of the most prevalent causes of death that can be treated more effectively and lead to fewer fatalities if identified early. Heart muscle fibrosis affects the diastolic and systolic function of the heart and is linked to unfavorable cardiovascular outcomes. Cardiac magnetic resonance (CMR) scarring, a risk factor for ischemic heart disease, may be accurately identified by magnetic resonance imaging (MRI) to recognize fibrosis. In the past few decades, numerous methods based on MRI have been employed to identify and categorize cardiac fibrosis. Because they increase the therapeutic advantages and the likelihood that patients will survive, developing these approaches is essential and has significant medical benefits. A brand-new method that uses MRI has been suggested to help with diagnosing. Advances in deep learning (DL) networks contribute to the early and accurate diagnosis of heart muscle fibrosis. This study introduces a new deep network known as FibrosisNet, which detects and classifies fibrosis if it is present. It includes some of 17 various series layers to achieve the fibrosis detection target. The introduced classification system is trained and evaluated for the best performance results. In addition, deep transfer-learning models are applied to the different famous convolution neural networks to find fibrosis detection architectures. The FibrosisNet architecture achieves an accuracy of 96.05%, a sensitivity of 97.56%, and an F1-Score of 96.54%. The experimental results show that FibrosisNet has numerous benefits and produces higher results than current state-of-the-art methods and other advanced CNN approaches. [ABSTRACT FROM AUTHOR]

This editorial discusses the advancements in diagnostic imaging technology for cardiothoracic disorders. It highlights the development of new hardware, such as spectral imaging and digital PET/CT technology, which improve image quality and reduce radiation exposure for patients. The formulation of new clinical protocols and the analysis of diagnostic images through radiomics are also discussed. The introduction of artificial intelligence and machine learning algorithms in postprocessing software is seen as a promising development. The editorial emphasizes the importance of these innovations in improving the diagnosis and treatment of cardiothoracic diseases, which have a significant impact on global mortality and healthcare costs. [Extracted from the article]