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

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.