Clinical predictors of drug-resistant tuberculosis in Mexico

Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study

Background

Pulmonary nodular consolidation (PN) and pulmonary cavity (PC) may represent the two most promising imaging signs in differentiating multidrug-resistant (MDR)-pulmonary tuberculosis (PTB) from drug-sensitive (DS)-PTB. However, there have been concerns that literature described radiological feature differences between DS-PTB and MDR-PTB were confounded by that MDR-PTB cases tend to have a longer history. This study seeks to further clarify this point.

Methods

All cases were from the Guangzhou Chest Hospital, Guangzhou, China. We retrieved data of consecutive new MDR cases [n=46, inclusive of rifampicin-resistant (RR) cases] treated during the period of July 2020 and December 2021, and according to the electronic case archiving system records, the main PTB-related symptoms/signs history was ≤3 months till the first computed tomography (CT) scan in Guangzhou Chest Hospital was taken. To pair the MDR-PTB cases with assumed equal disease history length, we additionally retrieved data of 46 cases of DS-PTB patients. Twenty-two of the DS patients and 30 of the MDR patients were from rural communities. The first CT in Guangzhou Chest Hospital was analysed in this study. When the CT was taken, most cases had anti-TB drug treatment for less than 2 weeks, and none had been treated for more than 3 weeks.

Results

Apparent CT signs associated with chronicity were noted in 10 cases in the DS group (10/46) and 9 cases in the MDR group (10/46). Thus, the overall disease history would have been longer than the assumed <3 months. Still, the history length difference between DS patients and MDR patients in the current study might not be substantial. The lung volume involvement was 11.3%±8.3% for DS cases and 8.4%±6.6% for MDR cases (P=0.022). There was no statistical difference between DS cases and MDR cases both in PN prevalence and in PC prevalence. For positive cases, MDR cases had more PN number (mean of positive cases: 2.63 vs. 2.28, P=0.38) and PC number (mean of positive cases: 2.14 vs. 1.38, P=0.001) than DS cases. Receiver operating characteristic curve analysis shows, PN ≥4 and PC ≥3 had a specificity of 86% (sensitivity 25%) and 93% (sensitivity 36%), respectively, in suggesting the patient being a MDR cases.

Conclusions

A combination of PN and PC features allows statistical separation of DS and MDR cases.

Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality. In recent years, imaging diagnosis of DR-TB has developed rapidly, but there is a lack of consistent understanding. To this end, the Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Infectious Diseases Group of Chinese Medical Association of Radiology; Digital Health Committee of China Association for the Promotion of Science and Technology Industrialization, and other organizations, formed a group of TB experts across China. The conglomerate then considered the Chinese and international diagnosis and treatment status of DR-TB, China's clinical practice, and evidence-based medicine on the methodological requirements of guidelines and standards. After repeated discussion, the expert consensus of imaging diagnosis of DR-PB was proposed. This consensus includes clinical diagnosis and classification of DR-TB, selection of etiology and imaging examination [mainly X-ray and computed tomography (CT)], imaging manifestations, diagnosis, and differential diagnosis. This expert consensus is expected to improve the understanding of the imaging changes of DR-TB, as a starting point for timely detection of suspected DR-TB patients, and can effectively improve the efficiency of clinical diagnosis and achieve the purpose of early diagnosis and treatment of DR-TB.

Chest X-ray Features in Drug-Resistant Tuberculosis Patients in Nigeria; a Retrospective Record Review

Chest X-ray (CXR) characteristics of patients with drug-resistant tuberculosis (DR-TB) depend on a variety of factors, and therefore, identifying the influence of these factors on the appearance of DR-TB in chest X-rays can help physicians improve diagnosis and clinical suspicion. Our aim was to describe the CXR presentation of patients with DR-TB and its association with clinical and demographic factors. A retrospective analysis of the CXRs of DR-TB patients in Nigeria between 2010 and 2016 was performed, reviewing features of chest radiographs, such as cavitation, opacity and effusion, infiltration and lung destruction. The association of these abnormal CXR findings with clinical and demographic characteristics was evaluated using bivariate and multivariate models, and a p-value < 0.05 was considered statistically significant with a 95% confidence interval. A total of 2555 DR-TB patients were studied, the majority (66.9%) were male, aged 29−38 years (36.8%), previously treated (77%), from the South West treatment zone (43.5%), HIV negative (76.7%) and bacteriologically diagnosed (89%). X-ray findings were abnormal in 97% of the participants, with cavitation being the most common (41.5%). Cavitation, effusion, fibrosis, and infiltration were higher in patients presenting in the South West zone and in those previously treated for DR-TB, while lung destruction was significantly higher in patients who are from the South South zone, and in those previously treated for DR-TB. Patients from the South East zone (AOR: 6.667, 95% CI: 1.383−32.138, p = 0.018), the North East zone (AOR: 6.667, 95% CI: 1.179−37.682, p = 0.032) and the North West zone (AOR: 6.30, 95% CI: 1.332−29.787, p = 0.020) had a significantly increased likelihood of abnormal chest X-ray findings, and prior TB treatment predisposed the patient to an increased likelihood of abnormal chest X-ray findings compared to new patients (AOR: 8.256, 95% CI: 3.718−18.330, p = 0.001). The finding of a significantly higher incidence of cavities, effusions and fibrosis in DR-TB patients previously treated could indicate late detection or presentation with advanced DR-TB disease, which may require a more individualized regimen or surgical intervention.

Impact of alcohol drinking and tobacco smoking on the drug-resistance of newly diagnosed tuberculosis: a retrospective cohort study in Shandong, China, during 2004-2020

OBJECTIVES

To investigate the independent and collective impact of alcohol drinking and tobacco smoking on the drug-resistance of newly diagnosed tuberculosis (TB).

DESIGN

This was a retrospective cohort study.

SETTING

Shandong, China.

PARTICIPANTS

Patients with newly diagnosed TB from 1 January 2004 to 31 December 2020 were collected. Exclusive criteria: retreated cases; extrapulmonary tuberculosis; without information on drug susceptibility testing results, smoking or drinking habits; bacteriological identification as non-tuberculous mycobacteria.

PRIMARY AND SECONDARY OUTCOME MEASURES

Patients were classified into four groups including smokers only (G₁), drinker only (G₂), smoker +drinker (G₃), non-smoker +non-drinker group (G₀). We described the drug-resistant profiles, clinical factors and calculated the ORs of different drug-resistance among G₁, G₂, G₃, compared with G₀ through univariate and multivariate logistics regression models.

RESULTS

Of the 7996 TB cases enrolled, the proportions of G₁, G₂, G₃ and G₀ were 8.25%, 3.89%, 16.46% and 71.40%, respectively. The rates of drug-resistant (DR)-TB, mono-resistant TB, multidrug resistant (MDR)-TB, polydrug resistant TB in G_1, G₂, G₃ and G₀ were 19.24%/16.4%/17.33%/19.08%, 11.52%/8.68%/10.94%/11.63%, 3.03%/2.57%/2.96%/3.66% and 4.70%/4.82%/3.34%/ 4.08%, respectively. G₃ had a higher risk of MDR1: isoniazid +rifampin (adjusted OR (aOR)=1.91, 95% CI: 1.036 to 3.532), but had a lower risk of DR-TB (aOR=0.84, 95% CI: 0.71 to 0.99), rifampin-related resistance (aOR=0.68, 95% CI: 0.49 to 0.93), streptomycin-related resistance (aOR=0.82, 95% CI: 0.68 to 0.99), ethambutol-related resistance (aOR=0.57, 95% CI: 0.34 to 0.95), MDR3: isoniazid +rifampin+streptomycin (aOR=0.41, 95% CI: 0.19 to 0.85), any isoniazid +streptomycin resistance (aOR=0.85, 95% CI: 0.71 to 1.00). However, there were no significant differences between G₁ and G₀, G₂ and G₀ in all drug-resistant subtypes. Those patients with cavity had a higher risk of DR-TB among G₃ (OR=1.35, 95% CI: 1.01 to 1.81).

CONCLUSION

Although we did not found an independent impact of alcohol drinking or tobacco smoking on TB drug-resistance, respectively, these two habits had a combined effect on TB drug-resistance.

Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays

Classification of drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) from chest radiographs remains an open problem. Our previous cross validation performance on publicly available chest X-ray (CXR) data combined with image augmentation, the addition of synthetically generated and publicly available images achieved a performance of 85% AUC with a deep convolutional neural network (CNN). However, when we evaluated the CNN model trained to classify DR-TB and DS-TB on unseen data, significant performance degradation was observed (65% AUC). Hence, in this paper, we investigate the generalizability of our models on images from a held out country's dataset. We explore the extent of the problem and the possible reasons behind the lack of good generalization. A comparison of radiologist-annotated lesion locations in the lung and the trained model's localization of areas of interest, using GradCAM, did not show much overlap. Using the same network architecture, a multi-country classifier was able to identify the country of origin of the X-ray with high accuracy (86%), suggesting that image acquisition differences and the distribution of non-pathological and non-anatomical aspects of the images are affecting the generalization and localization of the drug resistance classification model as well. When CXR images were severely corrupted, the performance on the validation set was still better than 60% AUC. The model overfitted to the data from countries in the cross validation set but did not generalize to the held out country. Finally, we applied a multi-task based approach that uses prior TB lesions location information to guide the classifier network to focus its attention on improving the generalization performance on the held out set from another country to 68% AUC.

Differentiating between drug-sensitive and drug-resistant tuberculosis with machine learning for clinical and radiological features

Background

Tuberculosis (TB) drug resistance is a worldwide public health problem that threatens progress made in TB care and control. Early detection of drug resistance is important for disease control, with discrimination between drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) still being an open problem. The objective of this work is to investigate the relevance of readily available clinical data and data derived from chest X-rays (CXRs) in DR-TB prediction and to investigate the possibility of applying machine learning techniques to selected clinical and radiological features for discrimination between DR-TB and DS-TB. We hypothesize that the number of sextants affected by abnormalities such as nodule, cavity, collapse and infiltrate may serve as a radiological feature for DR-TB identification, and that both clinical and radiological features are important factors for machine classification of DR-TB and DS-TB.

Methods

We use data from the NIAID TB Portals program (https://tbportals.niaid.nih.gov), 1,455 DR-TB cases and 782 DS-TB cases from 11 countries. We first select three clinical features and 26 radiological features from the dataset. Then, we perform Pearson's chi-squared test to analyze the significance of the selected clinical and radiological features. Finally, we train machine classifiers based on different features and evaluate their ability to differentiate between DR-TB and DS-TB.

Results

Pearson's chi-squared test shows that two clinical features and 23 radiological features are statistically significant regarding DR-TB vs. DS-TB. A ten-fold cross-validation using a support vector machine shows that automatic discrimination between DR-TB and DS-TB achieves an average accuracy of 72.34% and an average AUC value of 78.42%, when combing all 25 statistically significant features.

Conclusions

Our study suggests that the number of affected lung sextants can be used for predicting DR-TB, and that automatic discrimination between DR-TB and DS-TB is possible, with a combination of clinical features and radiological features providing the best performance.

Identifying Drug-Resistant Tuberculosis in Chest Radiographs: Evaluation of CNN Architectures and Training Strategies

Tuberculosis (TB) is a serious infectious disease that mainly affects the lungs. Drug resistance to the disease makes it more challenging to control. Early diagnosis of drug resistance can help with decision making resulting in appropriate and successful treatment. Chest X-rays (CXRs) have been pivotal to identifying tuberculosis and are widely available. In this work, we utilize CXRs to distinguish between drug-resistant and drug-sensitive tuberculosis. We incorporate Convolutional Neural Network (CNN) based models to discriminate the two types of TB, and employ standard and deep learning based data augmentation methods to improve the classification. Using labeled data from NIAID TB Portals and additional non-labeled sources, we were able to achieve an Area Under the ROC Curve (AUC) of up to 85% using a pretrained InceptionV3 network.

Evaluation of the frequency of resistance to 2 drugs (Isoniazid and Rifampin) by molecular investigation and it's risk factors in new cases of smear positive pulmonary tuberculosis in health centers under the cover of Jundishapur University of Medical Sciences in 2017

Introduction:

Despite the great efforts to control tuberculosis (TB), the disease is still one of the major health challenges throughout the world. The basic treatment for TB is drug therapy. Currently, the main anti-tuberculosis drugs with major use in the treatment and control of the disease are isoniazid, rifampin, pyrazinamide, ethambutol, and streptomycin. One of the serious crises in controlling TB epidemic is diagnosis and treatment of patients with Multidrug Resistant Tuberculosis (MDR-TB MDR). The purpose of the study was to examine and evaluate the resistance of mycobacterium TB strains isolated from specimens of newly diagnosed smear positive pulmonary TB to isoniazid and rifampin using molecular methods and their risk factors.

Methods:

Sputum samples of newly diagnosed smear positive pulmonary TB patients were prepared, collected, and sent to Reference Laboratory in Ahvaz. DNA of mycobacterium tuberculosis was prepared from the samples using Qiagen kit according to the instructions of the manufacturing company. Isoniazid resistance was evaluated using specific primers for inhA and KatG genes. Rifampin resistance was evaluated using MAS-PCR method with three specific alleles of rpobB codons and codons 516, 526 and 531.

Results:

Mycobacterium tuberculosis resistance to Isoniazid was 7.3%, to Rifampin 5.5% and to both drugs 1.8%. In our study, there were no association between drug resistance and gender, age, prison history, smoking, drug use, underlying disease, occupation, and HIV.

Conclusion:

According our findings that include prevalence of 7.3% Isoniazide resistance, 5.5% Rifampin resistance and 1.8% to both drugs, evaluating all newly diagnosed patients for resistance to standard anti-tuberculosis treatment seems rational.