Health & Medicinearticle2026-09-03

Prospective outcomes of adults with speciated Mycobacterium avium complex lung disease, 2021-2026

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Abstract

Abstract Rationale Mycobacterium avium complex lung disease (MAC-LD) is clinically heterogeneous and carries diverse outcomes. Objectives To describe predictors of clinical progression of MAC-LD in a state-wide cohort. Methods We enrolled adults with MAC-LD from across Virginia, USA starting in 2021. Every 6 months we performed respiratory quality of life questionnaire, scored CT scans, and recorded respiratory mycobacterial cultures including MAC speciation. Outcomes were classified using NTM-NET consensus definitions, factors predicting clinical progression analyzed by Poisson regression, and hierarchical clustering on principal components derived from Factorial Analysis of Mixed Data. Measurements and Main Results Of 105 participants the median follow-up was 917 days. Mean age was 69.8 years, 79 (75%) were women, and 70 (67%) had nodular bronchiectasis. M. intracellulare was the most common species, present in 48 (46%) participants at enrollment, followed by M. avium (29, 28%), and M. intracellulare subspecies chimaera (11, 10%). Only 2 (9%) of 22 evaluable participants met the NTM-NET definition of cure. In all participants after multivariable adjustment, older baseline age (incidence rate ratio 1.03 [1, 1.06], p = 0.04) and fibrocavitary CT scan pattern (2.57 [1.33, 4.96], p = 0.005), were associated with unfavorable 12-month clinical progression. Species type and species persistence contributed to characteristics of three distinct phenotypes of MAC-LD of varying severity and clinical progression. Conclusions The majority of participants were not assessable for MAC-LD treatment outcomes using strict NTM-NET definitions. Species informed phenotypes of MAC lung disease are prognostically useful and can inform routine management and trial design.

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View paper (DOI)Open access versionOpenAlexAnnals of the American Thoracic SocietyPublished 2026-09-03

Authors: Scott K. Heysell, Sharon Johnson, Lea Becker, Prakruti Rao, Caroline Nguyen, Julianna Lefcofrides, Catherine Strawley, Suzanne Stroup, Girija Ramakrishnan, William Kleinot, Michele Scheurenbrand, Laura Young, Jasie Hearn, Myra Williams, Joseph Falkinham, Sharvari Narendra, Salvador Castañeda-Barba, Michael Hanley, Alan Ropp, Amy Mathers, Eric R. Houpt

Institutions: University of Virginia, Virginia Tech, Imaging Center, Virginia Department of Health, Virginia International University