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. 2021 Nov;15(11):1147-1157.
doi: 10.1111/crj.13420. Epub 2021 Aug 8.

Establishment and validation of a predictive model for nontuberculous mycobacterial infections in acid-fast bacilli smear-positive patients

Xianqiu Chen  1 , Yuan Zhang  1 , Jinfu Xu  1 , Huiping Li  1
Affiliations

Establishment and validation of a predictive model for nontuberculous mycobacterial infections in acid-fast bacilli smear-positive patients

Xianqiu Chen et al. Clin Respir J. 2021 Nov.

Abstract

Introduction: Nontuberculous mycobacteria (NTM) and pulmonary tuberculosis (PTB) are difficult to distinguish in initial acid-fast bacilli (AFB) smear-positive patients.

Objectives: Establish a predictive model to identify more effectively NTM infections in initial AFB patients.

Methods: Consecutive AFB smear-positive patients in the Respiratory Department of Shanghai Pulmonary Hospital from January 2019 to February 2020 were retrospectively analysed. A multivariate regression was used to determine the independent risk factors for NTM. A receiver operating characteristic (ROC) curve was used to determine the model's predictive discrimination. The model was validated internally by a calibration curve and externally for consecutive AFB smear-positive patients from March to June 2020 in this institution.

Results: Presenting with haemoptysis, bronchiectasis, a negative QuantiFERON tuberculosis (QFT) test and being female were characteristics significantly more common in patients with NTM (P ≤ 0.001), when compared with PTB. The involvement of right middle lobe, left lingual lobe and cystic change was more commonly seen on chest high-resolution computed tomography (HRCT) in patients with NTM (P < 0.05), compared with PTB. Multivariate regression showed female, bronchiectasis, negative test for QFT and right middle lobe lesion were independent risk factors for NTM (P < 0.05). A ROC curve showed a sensitivity and specificity of 85.9% and 93.4%, respectively, and the area under the curve (AUC) was 0.963. Moreover, internal and external validation both confirmed the effectiveness of the model.

Conclusions: The predictive model would be useful for early differential diagnosis of NTM in initial AFB smear-positive patients.

Keywords: QuantiFERON tuberculosis (QFT); acid-fast bacillus (AFB) smear; bronchiectasis; nontuberculous mycobacteria (NTM); pulmonary tuberculosis (PTB).

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Conflict of interest statement

The authors have no conflicts of interest to declare.

Figures

FIGURE 1
FIGURE 1
Study flow diagram. Abbreviations: AFB, acid‐fast bacillus; ATS, American Thoracic Society; NTM, nontuberculous mycobacteria; PTB, pulmonary tuberculosis
FIGURE 2
FIGURE 2
A ROC curve to predict NTM in AFB smear‐positive patients. A combination of female patient, bronchiectasis, negative test for QuantiFERON tuberculosis (QFT), right middle lobe lesion in chest CT yielded a ROC curve, with a sensitivity and specificity of 85.9% and 93.4%, respectively. The area under the curve (AUC) is 0.963, P < 0.001. Abbreviations: AFB, acid‐fast bacilli; CT, computed tomography; NTM, nontuberculous mycobacteria; ROC, receiver operating characteristic
FIGURE 3
FIGURE 3
Nomogram of the predictive modal. A nomogram of the predictive model to predict NTM in AFB smear‐positive patients using points of four binary variables: bronchiectasis, right middle lobe lesions, female and negative test for QFT. Draw a line perpendicular from the corresponding axis of each risk factor until it reaches the top line labelled ‘points’. Sum up the number of points for all risk factors then draw a line descending from the axis labelled ‘total points’ until it intercepts each of the survival axes to determine risk of NTM. Abbreviations: NTM, nontuberculous mycobacteria; PTB, pulmonary tuberculosis; QFT, QuantiFERON tuberculosis
FIGURE 4
FIGURE 4
Calibration curve. Calibration curve depicts the calibration of the predictive model in terms of the agreement between the predicted probabilities of NTM and actual NTM. The y‐axis represents the actual NTM rate. The x‐axis represents the predicted probabilities of NTM. The diagonal dotted line represents a prediction by an ideal model. The blue solid line represents the performance of the model. The line of bias corrected is generated automatically by the software after correcting the deviation to prevent overfitting. This line shall be most focused, of which a closer fit to the diagonal dotted line represents a better prediction. It was showed in this calibration curve that the probability of NTM predicted by the model was very close to the actual probability. Abbreviation: NTM, nontuberculous mycobacteria
FIGURE 5
FIGURE 5
External validation of the predictive model. (A) Screening process of cases for validation. (B) A ROC curve to evaluate the predictive effect of the model on external data, with an AUC of 0.913, P < 0.001. Abbreviations: AFB, acid‐fast bacillus; QFT, QuantiFERON tuberculosis
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