Obstructive Sleep Apnea Does Not Exclude Polycythemia Vera: A Case Report.

Introduction: Polycythemia vera (PV) is one of the myeloproliferative neoplasms (MPN) diagnosed by World Health Organization (WHO) criteria 2016, which requires the presence of 3 major criteria: high hemoglobin/hematocrit, bone marrow findings, and Janus Kinase 2 (JAK2) mutation or two major and one minor criteria, including erythropoietin (EPO) level. However, in clinical practice, difficulties in diagnosis can arise as it may be masked by secondary causes for erythrocytosis such as smoking or obstructive sleep apnea (OSA). Case Presentation: Here, we report a 55-year-old gentleman, morbidly obese with OSA on home continuous positive airway pressure (CPAP) machine, who was incidentally found to have polycythemia. Further evaluation confirmed the diagnosis of PV. Conclusion: PV can be masked by the assumption of secondary polycythemia based on history. This underscores the importance of screening such cohort through JAK2 and EPO testing to avoid missing PV.

Recurrence of acute chest syndrome post stopping Crizanlizumab, the dilemma of stopping vs continuation in patient with sickle cell disease: case report.

Sickle cell disease (SCD) is one of the most common hematological diseases, which results in variable complications. The treatment of SCD is evolving but limited options are available for now. Acute chest syndrome (ACS) is one of the serious complications observed in SCD and a challenging one in prevention. Crizanlizumab is a monoclonal antibody that binds to P-selectin and improves blood flow by preventing sickle cell adhesion to endothelium, resulting in improvement of vaso-oclusive crises (VOC). It is not well evaluated in terms of ACS prevention. Here we report a 23-year-old patient with SCD and recurrent ACS; she was started on Crizanlizumab and she had no more ACS, but once she was off Crizanlizumab she developed ACS again, later Crizanlizumab was re-started, and the patient has improved significantly.

Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.

Philadelphia-negative (Ph-) myeloproliferative neoplasms (MPNs) are a group of hematopoietic malignancies identified by clonal proliferation of blood cell lineages and encompasses polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF). The clinical and laboratory features of Philadelphia-negative MPNs are similar, making them difficult to diagnose, especially in the preliminary stages. Because treatment goals and progression risk differ amongst MPNs, accurate classification and prognostication are critical for optimal management. Artificial intelligence (AI) and machine learning (ML) algorithms provide a plethora of possible tools to clinicians in general, and particularly in the field of malignant hematology, to better improve diagnosis, prognosis, therapy planning, and fundamental knowledge. In this review, we summarize the literature discussing the application of AI and ML algorithms in patients with diagnosed or suspected Philadelphia-negative MPNs. A literature search was conducted on PubMed/MEDLINE, Embase, Scopus, and Web of Science databases and yielded 125 studies, out of which 17 studies were included after screening. The included studies demonstrated the potential for the practical use of ML and AI in the diagnosis, prognosis, and genomic landscaping of patients with Philadelphia-negative MPNs.