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High-Resolution CT Findings as Predictive Factors for Recurrent Nontuberculous Mycobacterial Pulmonary Disease after Successful Treatmentopen access

Authors
Choi, HyewonCha, Min JaeKim, Yang SooChoi, Jae Chol
Issue Date
Jan-2021
Publisher
MDPI
Keywords
nontuberculous mycobacteria; recurrence; computed tomography
Citation
JOURNAL OF CLINICAL MEDICINE, v.10, no.2
Journal Title
JOURNAL OF CLINICAL MEDICINE
Volume
10
Number
2
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/47610
DOI
10.3390/jcm10020172
ISSN
2077-0383
2077-0383
Abstract
Despite long-term treatment for nontuberculous mycobacterial pulmonary disease (NTM-PD), recurrence is common. We aim to identify computed tomography (CT) findings that predict recurrence after successful treatment of NTM-PD. This retrospective study included 44 patients (12 men, 60 +/- 11.2 years) successfully treated for NTM-PD between March 2009 and September 2016. Recurrence developed in 18 patients (40.9%) during follow-up (median, 852 days). CT scores for bronchiectasis, bronchiolitis, consolidation, cavities, and nodules at the initiation and termination of treatment were evaluated, then determined association with recurrence. We also assessed the diagnostic performance and reproducibility of CT scores. Patients with recurrent NTM-PD showed higher CT scores for bronchiectasis (p = 0.008), nodules (p = 0.006), consolidation (p = 0.033), and total CT scores (p = 0.017) at the time of treatment termination. On the contrary, only nodule score differed among the initial CT scores (p = 0.014). Regression analysis showed that the scores for bronchiectasis (odds ratio (OR) = 1.638, 95% confidence interval (CI) = 1.049-2.558, p = 0.030) and nodules (OR = 5.246, 95% CI = 1.370-20.087, p = 0.016) at treatment termination were significant predictors. The AUC of the regression model was 0.814 (95% CI = 0.689-0.939, p = 0.005). The interreader agreement for the total CT score was excellent (intraclass correlation coefficient = 0.841, p < 0.001). CT scores at the time of treatment termination can predict disease recurrence with good reproducibility.
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