Comprehensive Management Algorithm for Mycobacterium avium Complex Pulmonary Disease in the Real-World Setting.

Abstract

The management of Mycobacterium avium complex pulmonary disease (MAC-PD) is challenging due to limited efficacy and frequent adverse events associated with standard treatments. The 2020 guidelines from ATS, ERS, ESCMID, and IDSA provide recommendations, but real-world adherence is often hindered by these issues, leading many patients to be unable to complete standard therapy. This review proposes a comprehensive management algorithm for MAC-PD, emphasizing multidisciplinary approaches and integrating non-antimicrobial management before, during, and after drug treatment to enhance patient outcomes. When a patient presents with chronic respiratory symptoms suggestive of NTM, clinicians should follow a guideline-based approach to diagnosis, as diagnostic delays are common due to nonspecific symptoms. Proper evaluation should determine the disease phenotype (existence of cavitary lesions) as it influences treatment choices. Airway clearance, nutritional support, and management of underlying conditions are essential non-antimicrobial components. Regular outpatient monitoring helps detect disease progression and optimize treatment. Treatment strategies vary based on disease severity. For non-cavitary nodular bronchiectatic disease, a thrice-weekly regimen is preferred due to better tolerability. Severe cases or those with cavitary forms may require daily treatment with additional aminoglycosides. Amikacin liposome inhalation suspension (ALIS) is recommended for patients not responding to standard regimens after six months. Recent research addresses drug intolerance, suggesting alternatives like a two-drug regimen without rifamycin in certain cases. Consultation with NTM specialists is advised for complex cases, particularly those with macrolide resistance or requiring surgical intervention. The algorithm emphasizes shared decision-making, patient education, and family support to improve adherence and outcomes. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).