Initially described in 1936, non-bacterial thrombotic endocarditis (NBTE) is a rare entity involving sterile vegetations on cardiac valves. These vegetations are usually small and friable, typically associated with hypercoagulable states of malignancy and inflammatory diseases such as systemic lupus erythematosus. Diagnosis remains challenging and is commonly made post-mortem although standard clinical methods such as echocardiography (transthoracic and transesophageal) and magnetic resonance imaging may yield the clinical diagnosis. Prognosis of NBTE is poor with very high morbidity and mortality usually related to the serious underlying conditions and high rates of systemic embolization. Therapeutic anticoagulation with unfractionated heparin has been described as useful for short term prevention of recurrent embolic events in patients with NBTE but there are no guidelines for management of this disease.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. To effectively monitor the health of those affected, maintaining up-to-date health records is essential, and digital health informatics apps for surveillance play a pivotal role. In this review, we overview the existing literature on identifying and characterizing long COVID manifestations through hierarchical classification based on Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) initiative in artificial intelligence (AI) to identify long COVID. Through knowledge exploration, we present a concept map of clinical pathways for long COVID, which offers insights into the data required and explores innovative frameworks for health informatics apps for tackling the long-term effects of COVID-19. This study achieves two main objectives by comprehensively reviewing long COVID identification and characterization techniques, making it the first paper to explore incorporating long COVID as a variable risk factor within a digital health informatics application. By achieving these objectives, it provides valuable insights on long COVID’s challenges and impact on public health.

The ongoing COVID-19 pandemic has profoundly affected millions of lives globally, with some individuals experiencing persistent symptoms even after recovering. Understanding and managing the long-term sequelae of COVID-19 is crucial for research, prevention, and control. As a result, to monitor the health of individuals affected by these conditions, they must maintain up-to-date health records using digital health informatics apps for surveillance. In this review, we provide an overview of the existing literature on identifying long COVID manifestations through hierarchical classification and the characterization of long COVID by different hierarchical groups based on the Human Phenotype Ontology (HPO). We outline the aspects of the National COVID Cohort Collaborative (N3C) and Researching COVID to Enhance Recovery (RECOVER) in artificial intelligence (AI) to identify long COVID. Knowledge exploration, using the concept map for the clinical pathways of long COVID presented in this paper, provides an overview of the data needed to explore tackling the long-term effect of COVID-19 by integrating innovative cohesive frameworks and designing health informatics-based applications. To the best of our knowledge, this is the first paper to explore the potential incorporation of long COVID as a variable risk factor within a digital health informatics application.

In select patients with severe, eccentric mitral regurgitation (MR), we have observed that the regurgitant jet may entrain blood along the left atrial wall to produce a Chinese yin-yang symbol on color Doppler. Clinical Images from Journal of Invasive Cardiology. Cardiac Imaging MitraClip implantation transthoracic echocardiogram

Case Presentation A 49-year-old woman with a history of right breast cancer status post radiation therapy presented to our ED with increasing chest pain, exertional dyspnea, fatigue, and dizziness for several weeks. She denied syncope or near-syncope, and she had no personal or family history of cardiac disease. Her outpatient medications included tamoxifen and venlafaxine.