Lymphocyte-Directed Immunomodulation Remits Thymoma-Associated Autoimmune Pneumonitis.

BACKGROUND: Thymoma presents with several autoimmune manifestations and is associated with secondary autoimmune regulator (AIRE) deficiency. Pneumonitis has recently been described as an autoimmune manifestation associated with thymoma presenting with similar clinical, radiographic, histological, and autoantibody features as seen in patients with inherited AIRE deficiency who suffer from Autoimmune PolyEndocrinopathy-Candidiasis-Ectodermal Dystrophy (APECED) syndrome. OBJECTIVES: To treat two patients with biopsy-proven thymoma-associated pneumonitis with lymphocyte-directed immunomodulation. METHODS: Two patients with thymoma were enrolled on IRB-approved protocols at the NIH Clinical Center. We performed history and physical examination; laboratory, radiographic, histologic and pulmonary function evaluations; and measurement of the lung-directed autoantibodies KCNRG and BPIFB1 prior to and at 1- and 6-months following initiation of lymphocyte-directed immunomodulation with azathioprine with or without rituximab. RESULTS: Combination T- and B-lymphocyte-directed immunomodulation resulted in improvement of clinical, functional, and radiographic parameters at 6-month follow-up evaluations in both patients with sustained remission up to 12-36 months following treatment initiation. CONCLUSION: Lymphocyte-directed immunomodulation remitted autoimmune pneumonitis in two patients with thymoma.

A qualitative study of big data and the opioid epidemic: recommendations for data governance.

BACKGROUND: The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. OBJECTIVES: We aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical ways. METHODS: We conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health. RESULTS: Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches. CONCLUSIONS: Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions.