Post-treatment Radiographic Severity and Mortality in Mycobacterium avium Complex Pulmonary Disease

Rationale: Imaging studies are widely performed when treating Mycobacterium avium complex pulmonary disease (MAC-PD); however, the clinical significance of post-treatment radiographic change is unknown. Objectives: To determine whether a deep neural network trained with pulmonary tuberculosis could adequately score the radiographic severity of MAC-PD and then to examine relationships between post-treatment radiographic severity and its change from baseline and long-term prognosis. Methods: We retrospectively collected chest radiographs of adult patients with MAC-PD treated for ⩾6 months at baseline and at 3, 6, 9, and 12 months of treatment. We correlated the radiographic severity score generated by a deep neural network with visual and clinical severity as determined by radiologists and mycobacterial culture status, respectively. The associations between the score, improvement from baseline, and mortality were analyzed using Cox proportional hazards regression. Results: In total, 342 and 120 patients were included in the derivation and validation cohorts, respectively. The network's severity score correlated with radiologists' grading (Spearman coefficient, 0.40) and mycobacterial culture results (odds ratio, 1.02; 95% confidence interval [CI], 1.0-1.05). A significant decreasing trend in the severity score was observed over time (P < 0.001). A higher score at 12 months of treatment was independently associated with higher mortality (adjusted hazard ratio, 1.07; 95% CI, 1.03-1.10). Improvements in radiographic scores from baseline were associated with reduced mortality, regardless of culture conversion (adjusted hazard ratio, 0.42; 95% CI, 0.22-0.80). These findings were replicated in the validation cohort. Conclusions: Post-treatment radiographic severity and improvement from baseline in patients with MAC-PD were associated with long-term survival.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:21

Enthalten in:

Annals of the American Thoracic Society - 21(2024), 2 vom: 01. Feb., Seite 235-242

Sprache:

Englisch

Beteiligte Personen:

Kim, Joong-Yub [VerfasserIn]
Lee, Seowoo [VerfasserIn]
Park, Hyungin [VerfasserIn]
Kim, Hyung-Jun [VerfasserIn]
Lee, Hyun Woo [VerfasserIn]
Lee, Jae Ho [VerfasserIn]
Yim, Jae-Joon [VerfasserIn]
Kwak, Nakwon [VerfasserIn]
Yoon, Soon Ho [VerfasserIn]

Links:

Volltext

Themen:

Artificial intelligence
Deep learning
Journal Article
Mortality
Nontuberculous mycobacteria
Radiograph

Anmerkungen:

Date Completed 02.02.2024

Date Revised 02.02.2024

published: Print

Citation Status MEDLINE

doi:

10.1513/AnnalsATS.202305-407OC

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM362821801