Disseminated Mycobacterium avium Complex Myositis in a Patient With Graft-Versus-Host Disease.

Mycobacterium avium complex (MAC) is a ubiquitous environmental pathogen that was infrequently reported as a cause of disease before the human immunodeficiency virus (HIV)/acquired immune deficiency syndrome epidemic. We present a case of MAC pyomyositis and bacteremia in a 59-year-old man with chronic lymphocytic leukemia in remission after an allogenic stem cell transplant. His posttransplant course was complicated by graft-versus-host disease, requiring treatment with oral steroids and ruxolitinib. In this report, we review the literature on disseminated MAC infection in patients with and without HIV. We also propose a potential mechanism by which this patient may have developed disseminated disease. Disseminated MAC myositis is uncommon in persons without HIV and requires a high index of suspicion for timely diagnosis.

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.

INTRODUCTION: Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear. OBJECTIVE: The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning. DESIGN: The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise. RESULTS: The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices. CONCLUSION: Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing.