Retrospective analysis of patients with non-tuberculous mycobacteria from a primary hospital in Southeast China

The Study of Associated Factors for Non-Tuberculous Mycobacterial Pulmonary Disease Compared to Pulmonary Tuberculosis: A Propensity Score Matching Analysis

Objective

Investigate the differences in clinical manifestations, imaging features, and associated inflammatory markers between Nontuberculous Mycobacterial Pulmonary Disease (NTM-PD) and Pulmonary Tuberculosis (PTB), identify potential risk factors for NTM-PD, and establish a logistic regression model to evaluate its diagnostic value.

Methods

Baseline data were collected from 145 patients with NTM-PD and 206 patients with PTB. Propensity score matching (PSM) was utilized to achieve a 1:1 match between the two groups, resulting in 103 matched pairs. The differences in comorbidities, imaging features, and inflammatory markers were compared between the two groups. Multivariate binary logistic regression analysis was conducted to identify independent influencing factors, and the diagnostic value of the established model was evaluated.

Results

After matching, significant differences were observed between the NTM-PD group and the PTB group in terms of diabetes, bronchiectasis, chronic obstructive pulmonary disease(COPD), cystic and columnar changes, lung cavity presentation, and monocyte percentage (MONO%), lymphocyte count (LYMPH#), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) (P<0.05). Logistic regression analysis confirmed that diabetes, bronchiectasis, COPD, and lung cavities were risk factors for NTM-PD. The established regression analysis model was analyzed by the Receiver Operating Characteristic (ROC) curve, the Area Under the Curve (AUC) was obtained as 0.795 (P<0.001, 95% CI 0.734-0.857). At a Youden index of 0.505, the sensitivity was 84.5% and the specificity was 66.6%. The Hosmer-Lemeshow test was used to evaluate the model's calibration, with a chi-square value of 11.023 and P=0.200>0.05, indicating no significant difference between predicted and observed values.

Conclusion

For patients without diabetes but with bronchiectasis, COPD, and imaging characteristics of lung cavities, a high level of vigilance and active differential diagnosis for NTM-PD should be exercised. Given that the clinical manifestations of NTM-PD are similar to those of PTB, a detailed differential diagnosis is necessary during the diagnostic process to avoid misdiagnosis.

T-SPOT with CT image analysis based on deep learning for early differential diagnosis of nontuberculous mycobacteria pulmonary disease and pulmonary tuberculosis

OBJECTIVES

This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB).

METHODS

A total of 1049 cases were enrolled, including 467 NTM-PD and 582 PTB cases. A total of 320 cases (160 NTM-PD and 160 PTB) were randomized as the testing set and were analyzed using T-SPOT combined with the DL model. The testing cases were first divided into T-SPOT-positive and -negative groups, and the DL model was then used to separate the cases into four subgroups further.

RESULTS

The precision was found to be 91.7% for the subgroup of T-SPOT-negative and DL classified as NTM-PD, and 89.8% for T-SPOT-positive and DL classified as PTB, which covered 66.9% of the total cases, compared with the accuracy rate of 80.3% of T-SPOT alone. In the other two remaining groups, where the T-SPOT prediction was inconsistent with the DL model, the accuracy was 73.0% and 52.2%, separately.

CONCLUSION

Our study shows that the new diagnostic system combining T-SPOT with DL based computed tomography image analysis can greatly improve the classification precision of NTM-PD and PTB when the two methods of prediction are consistent.

Profile of Nontuberculous Mycobacteria in Patients Suspected of Tuberculosis and Drug-Resistant Tuberculosis

Objective  Infections due to nontuberculous mycobacteria (NTM) is increasing globally and may present as drug-resistant tuberculosis (DRTB). In India, data on NTM prevalence and species diversity is limited. Present study was conducted to detect the prevalence and profile of NTM among patients suspected of DRTB using paraffin slide culture (PSC)and mycobacteria growth indicator tube (MGIT) culture methods for isolation of NTM. Material and Method  A total of 2,938 samples suspected of TB/DRTB were cultured on PSC and MGIT960. Species identification of mycobacterial isolate was done by sequencing of 16s ribosomal RNA gene. Result  Among 2938 samples, 35 (1.19%) were found positive for NTM by PSC and 9 (0.30%) were found positive by MGIT. The diversity of NTM species was high (13 species). Out of 35 NTM isolates by PSC, maximum 34.29% (12) isolates were found to be Mycobacterium fortuitum , followed by 11.43% (4) Mycobacterium abscessus and Mycobacterium chelonae , and 42.85% (15) were other species viz. 8.57% (3) were Mycobacterium intracellulare and Mycobacterium kansasii , 5.71% (2) were Mycobacterium peregrinum , and 2.85% (1) were Mycobacterium flavescens, Mycobacterium farcinogenes, Mycobacterium moriokanese, Mycobacterium wolinskyi, Mycobacterium simiae, Mycobacterium goodii , and Mycobacterium terrae each. Coinfection of Mycobacterium tuberculosis (MTB) and NTM was found in 60% (21) samples. Conclusion  Prevalence of NTM was low among multidrug resistant tuberculosis/TB suspected patients, similar to other studies done in India. PSC was found better than MGIT for the isolation of NTM, though poor separation of NTM and MTB on subculture may have led to false negativity in cases of coinfection. About 13 species were isolated; M. fortuitum was the most common of all. Since coinfection of NTM and TB can also occur, samples of patients suspected of NTM should be cultured on PSC even if positive for MTB.