Characteristics of tuberculosis patients in the integrated tuberculosis control model in Chongqing, China: a retrospective study

Integrating untargeted and targeted LC-MS-based metabolomics to identify the serum metabolite biomarkers for tuberculosis

Given the limitations of untargeted metabolomics in precise metabolite quantification, our current research employed a novel approach by integrating untargeted and targeted metabolomics utilizing ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS) to analyze the metabolic profile and potential biomarkers for tuberculosis (TB). A cohort of 36 TB patients and 36 healthy controls (HC) was enlisted to obtain serum samples. Multivariate pattern recognition and univariate statistical analysis were employed to screen and elucidate the differential metabolites, whereas dot plots and receiver operating characteristic (ROC) curves were established for the identification of potential biomarkers of TB. The results indicated a distinct differentiation between the two groups, identifying 99 metabolites associated with five primary metabolic pathways in relation to TB. Of these, 19 metabolites exhibited high levels of sensitivity and specificity, as evidenced by the area under curve values approaching 1. Following targeted quantitative analysis, three potential metabolites, namely, L-asparagine, L-glutamic acid, and arachidonic acid, were demonstrated excellent discriminatory ability as evidenced by the results of the ROC curve, dot plots, and random forest model. Particularly noteworthy was the enhanced diagnostic efficacy of the combination of these three metabolites compared to singular biomarkers, suggesting their potential utility as serum biomarkers for TB diagnosis.

Challenge of ending TB in China: tuberculosis control in primary healthcare sectors under integrated TB control model-a systematic review and meta-analysis

BACKGROUND

China has the third-largest burden of tuberculosis (TB) cases in the world with great challenges towards ending TB. Primary health care (PHC) sectors play a critical role in TB prevention and control in communities under the Chinese integrated TB control model. However, there is a lack of comprehensive review of research evidence on TB control in PHC sectors under the integrated TB control model in China.

METHODS

This review was conducted following the PRISMA guidelines. Articles published from 2012 to January 2022 were searched from four international and three Chinese databases. Studies conducted inside mainland China and relevant with TB control service in PHC sectors under the integrated model were included. After study selection, data extraction, and quality assessment, the meta-analysis was performed with RevMan using a random-effect model.When I2 was more than 50%, subgroup analysis was performed to explore possible reasons for heterogeneity. We also conducted a post hoc sensitivity analysis for outcomes after meta-analysis by exclusion of studies with a high risk of bias or classified as low quality.

RESULTS

Forty-three studies from 16 provinces/municipalities in China were included in this review, and most studies included were of medium quality. PHC sectors in East China delivered TB control service better overall than that in West China, especially in tracing of patients and TB case management (TCM). In meta-analyses, both the pooled arrival rate of tracing and pooled TCM rate in East China were higher than those in West China. TB patients had a low degree of willingness to receive TCM provided by healthcare workers in PHC sectors nationwide, especially among migrant TB patients. There were 9 studies reporting factors related to TB control service in PHC sectors, 6 (2 in East and 4 in West China) of which indentified several characteristics of patients as associated factors. The context of PHC sectors was demonstrated to influence delivery of TB control service in PHC sectors in 5 studies (3 in East, 1 in Middle and 1 in West China). Most studies on strategies to promoting TB control services in PHC sectors were conducted in East China and some of these studies identified several online and offline interventions and strategies improving patients' treatment compliance [pooled OR (95% CI): 7.81 (3.08, 19.19] and awareness of TB [pooled OR (95% CI): 6.86 (2.16, 21.72)].

CONCLUSION

It is of urgent need to improve TB control in PHC sector in China, particularly in West China. Formative and implementation research with rigorous design are necessary to develop comprehensive, context-specific, and patient-centered TB control strategies to promote ending TB in China.

Paradoxical tuberculosis-associated immune reconstitution inflammatory syndrome in initiating ART among HIV-Infected patients in China-risk factors and management

BACKGROUND

China is a country burdened with a high incidence of both tuberculosis (TB) and HIV, Paradoxical tuberculosis-associated immune reconstitution inflammatory syndrome (TB-IRIS) is an important early complication in TB and HIV co-infected patients, but data from China are limited. Additionally, as an integrase strand transfer inhibitor (INSTI)-based antiretroviral therapy (ART) regimen becomes the first-line treatment, concerns have arisen regarding the potential increase in the incidence of paradoxical TB-IRIS. Nevertheless, the existing data are inconclusive and contradictory.

METHODS

We conducted a retrospective study at Chongqing Public Health Clinical Center from January 2018 to December 2021. We collected demographic and clinical data of HIV/TB co-infected patients who initiated ART. We described the patient characteristics, identified predictors for TB-IRIS, and determined clinical outcomes. The Statistical Package for Social Science (SPSS 25) was used to analyse the data. Continuous variables were compared using Student's t-test or rank sum test. Counting data were compared using the chi-square test or Fisher's exact test. The variables with statistical significance in the univariate analysis were added to the binary logistic regression. A p-value less than 0.05 was considered statistically significant.

RESULTS

A total of 384 patients co-infected with naive HIV and pulmonary TB (PTB) who were given ATT and ART combination were included. 72 patients (18.8%) developed paradoxical TB-IRIS with a median of 15 (12, 21) days after initiating ART. Baseline age ≤ 40years, CD4 + T-cell counts ≤ 50cells/µL, HIV viral load ≥ 500,000 copies/mL were found to be significantly associated with development of paradoxical TB-IRIS. Mortality rates were similar in the TB-IRIS (n = 5, 6.9%) group and non-TB-IRIS (n = 13, 4.2%) group. Interestingly, CD4+ T-cell counts recovery post-ART was significant higher in the TB-IRIS group when compared to the non-TB-IRIS group at the end of 24 weeks (P = 0.004), as well as at 48 weeks (P = 0.015). In addition, we consider that INSTI- based ART regimen do not increased the risk of Paradoxical TB-IRIS.

CONCLUSION

Paradoxical TB-IRIS, while often leading to clinical deterioration and hospitalization, is generally manageable. It appears to have a positive impact on the recovery of CD4 + T-cell counts over time. Importantly, our data suggest that INSTI-based ART regimens do not elevate the risk of TB-IRIS. Thus, paradoxical TB-IRIS should not be considered an impediment to initiating ART in adults with advanced immunodeficiency, except in the case of tuberculous meningitis (TBM).

The role of self-esteem as moderator of the relationship between experienced stigma and anxiety and depression among tuberculosis patients

Anxiety and depression are very common in tuberculosis (TB) patients and can adversely affect TB treatment adherence, ultimately leading to higher morbidity, mortality and drug resistance. Therefore, the aim of this study was to identify the association among experienced stigma, self-esteem and anxiety and depression, and to further explore whether self-esteem could be a moderator in the association between experienced stigma and anxiety and depression in TB patients. A total of 473 TB patients from Dalian, Liaoning Province, Northeast China participated in a cross-sectional survey. A structured questionnaire was developed to collect data. Hierarchical multiple regression was used to analyze the association among experienced stigma, self-esteem and experienced stigma × self-esteem interaction with anxiety and depression. Simple slope analysis was applied to visualize the interaction. Experienced stigma was positively associated with anxiety (B = 0.307, P < 0.01) and depression (B = 0.277, P < 0.01), and self-esteem was negatively associated with anxiety (B = - 0.215, P < 0.01) and depression (B = - 0.351, P < 0.01) in TB patients. The association between experienced stigma and anxiety was different in the low (1 standard deviation (SD) below the mean, B = 0.376, standard error (SE) = 0.056, P < 0.01) and high (1 SD above the mean, B = 0.228, SE = 0.060, P < 0.01) groups of self-esteem. Additionally, the association between experienced stigma and depression was also different in the low (1 SD below the mean, B = 0.363, SE = 0.053, P < 0.01) and high (1 SD above the mean, B = 0.179, SE = 0.056, P < 0.01) groups of self-esteem. Self-esteem could moderate the association between experienced stigma and anxiety and depression. In addition to reducing experienced stigma, enhancing self-esteem as a way to reduce the impact of experienced stigma on anxiety and depression can also help improve the mental health of TB patients.

Clinical assistant decision-making model of tuberculosis based on electronic health records

BACKGROUND

Tuberculosis is a dangerous infectious disease with the largest number of reported cases in China every year. Preventing missed diagnosis has an important impact on the prevention, treatment, and recovery of tuberculosis. The earliest pulmonary tuberculosis prediction models mainly used traditional image data combined with neural network models. However, a single data source tends to miss important information, such as primary symptoms and laboratory test results, that is available in multi-source data like medical records and tests. In this study, we propose a multi-stream integrated pulmonary tuberculosis diagnosis model based on structured and unstructured multi-source data from electronic health records. With the limited number of lung specialists and the high prevalence of tuberculosis, the application of this auxiliary diagnosis model can make substantial contributions to clinical settings.

METHODS

The subjects were patients at the respiratory department and infectious cases department of a large comprehensive hospital in China between 2015 to 2020. A total of 95,294 medical records were selected through a quality control process. Each record contains structured and unstructured data. First, numerical expressions of features for structured data were created. Then, feature engineering was performed through decision tree model, random forest, and GBDT. Features were included in the feature exclusion set as per their weights in descending order. When the importance of the set was higher than 0.7, this process was concluded. Finally, the contained features were used for model training. In addition, the unstructured free-text data was segmented at the character level and input into the model after indexing. Tuberculosis prediction was conducted through a multi-stream integration tuberculosis diagnosis model (MSI-PTDM), and the evaluation indices of accuracy, AUC, sensitivity, and specificity were compared against the prediction results of XGBoost, Text-CNN, Random Forest, SVM, and so on.

RESULTS

Through a variety of characteristic engineering methods, 20 characteristic factors, such as main complaint hemoptysis, cough, and test erythrocyte sedimentation rate, were selected, and the influencing factors were analyzed using the Chinese diagnostic standard of pulmonary tuberculosis. The area under the curve values for MSI-PTDM, XGBoost, Text-CNN, RF, and SVM were 0.9858, 0.9571, 0.9486, 0.9428, and 0.9429, respectively. The sensitivity, specificity, and accuracy of MSI-PTDM were 93.18%, 96.96%, and 96.96%, respectively. The MSI-PTDM prediction model was installed at a doctor workstation and operated in a real clinic environment for 4 months. A total of 692,949 patients were monitored, including 484 patients with confirmed pulmonary tuberculosis. The model predicted 440 cases of pulmonary tuberculosis. The positive sample recognition rate was 90.91%, the false-positive rate was 9.09%, the negative sample recognition rate was 96.17%, and the false-negative rate was 3.83%.

CONCLUSIONS

MSI-PTDM can process sparse data, dense data, and unstructured text data concurrently. The model adds a feature domain vector embedding the medical sparse features, and the single-valued sparse vectors are represented by multi-dimensional dense hidden vectors, which not only enhances the feature expression but also alleviates the side effects of sparsity on the model training. However, there may be information loss when features are extracted from text, and adding the processing of original unstructured text makes up for the error within the above process to a certain extent, so that the model can learn data more comprehensively and effectively. In addition, MSI-PTDM also allows interaction between features, considers the combination effect between patient features, adds more complex nonlinear calculation considerations, and improves the learning ability of the model. It has been verified using a test set and via deployment within an actual outpatient environment.

Transmission and Drug Resistance Genotype of Multidrug-Resistant or Rifampicin-Resistant Mycobacterium tuberculosis in Chongqing, China

Multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) is a global barrier for the Stop TB plan. To identify risk factors for treatment outcome and cluster transmission of MDR/RR-TB, whole-genome sequencing (WGS) data of isolates from patients of the Chongqing Tuberculosis Control Institute were used for phylogenetic classifications, resistance predictions, and cluster analysis. A total of 223 MDR/RR-TB cases were recorded between 1 January 2018 and 31 December 2020. Elderly patients and those with lung cavitation are at increased risk of death due to MDR/RR-TB. A total of 187 MDR/RR strains were obtained from WGS data; 152 were classified as lineage 2 strains. Eighty (42.8%) strains differing by a distance of 12 or fewer single nucleotide polymorphisms were classified as 20 genomic clusters, indicating recent transmission. Patients infected with lineage 2 strains or those with occupations listed as "other" are significantly associated with a transmission cluster of MDR/RR-TB. Analysis of resistant mutations against first-line tuberculosis drugs found that 76 (95.0%) of all 80 strains had the same mutations within each cluster. A total of 55.0% (44 of 80) of the MDR/RR-TB strains accumulated additional drug resistance mutations along the transmission chain, especially against fluoroquinolones (63.6% [28 of 44]). Recent transmission of MDR/RR strains is driving the MDR/RR-TB epidemics, leading to the accumulation of more serious resistance along the transmission chains. IMPORTANCE The drug resistance molecular characteristics of MDR/RR-TB were elucidated by genome-wide analysis, and risk factors for death by MDR/RR-TB were identified in combination with patient information. Cluster characteristics of MDR/RR-TB in the region were analyzed by genome-wide analysis, and risk factors for cluster transmission (recent transmission) were analyzed. These analyses provide reference for the prevention and treatment of MDR/RR-TB in Chongqing.

Development and validation of self-management scale for tuberculosis patients

Background

Tuberculosis remains a major threat to global public health. Regarding its control, directly observed therapy is not suitable as a global strategy for all tuberculosis patients. Self-management may be an important patient-centered tuberculosis case management supplement to directly observed therapy. However, there is currently no well-established instrument for measuring the self-management of tuberculosis patients. This study aimed to develop and validate a self-management scale for tuberculosis patients.

Methods

We developed an initial scale based on the tuberculosis health promotion indicators framework developed by our research group. After item analysis and two rounds of exploratory factor analysis, a final version of the scale was developed. A survey of 462 tuberculosis patients was conducted to develop and validate this scale. Cronbach’s α and intraclass correlation coefficients were used to assess reliability, and Pearson’s correlation coefficients were used to evaluate content validity. Fit indices, convergent validity, and discriminant validity were evaluated using confirmatory factor analysis to determine the construct validity of the scale.

Results

The scale was composed of 17 items in three dimensions (“adherence to treatment behavior,” “transmission prevention behavior,” and “supportive therapy behavior”). These three dimensions explained 76.60% of the variance. Cronbach’s α of the scale was 0.905, and the intraclass correlation coefficient was 0.897. Additionally, Pearson’s correlation analysis showed that each item was strongly correlated with the dimension to which it belonged (r = 0.849–0.915, p < 0.01). Most fit indices (Comparative Fit Index, Normed Fit Index, Incremental Fit Index, Goodness of fit index) reached the recommended threshold, and the average variance extracted values of the three dimensions were higher than 0.5. The values of the square root of the average variance extracted within each dimension were greater than the correlation between dimensions, and all heterotrait–monotrait values were below 0.85.

Conclusions

The self-management scale for tuberculosis patient demonstrated good reliability and validity and could be used as an instrument to evaluate the self-management of patients. Additionally, it could be used to develop evidence-based self-management interventions and evaluate those interventions.

Distribution of nontuberculous mycobacteria in patients with and without HIV/AIDS in Chongqing

INTRODUCTION

In this study, the distribution of nontuberculous mycobacteria (NTM) strains in patients with and without HIV/AIDS in Chongqing, China was evaluated.

METHODS

A retrospective study was performed in January-December 2020 at Chongqing Public Health Medical Center. NTM strains were assessed by a multi locus phylogenetic analysis. The distribution of NTM strains in HIV/AIDS and non-HIV/AIDS groups was compared. CD4+ cell counts, imaging changes, and characteristics of mycobacterial species were determined.

RESULTS

In total, 324 patients with NTM infection (50 patients with HIV/AIDS and 274 patients without HIV/AIDS) were included. The most common etiological agent was M. abscessus (29%), followed by M. paraintracellulare (12%) and M. colombiense (11%). Predominant NTM species were M. avium (26%), M. colombiense (24%), and M. kansasii (18%) in patients with HIV/AIDS and were M. abscessus (32%), M. paraintracellulare (13%), M. fortuitum (10%), and M. intracellulare (10%) in patients without HIV/AIDS. For a CD4+ cell count of <200/μl, the predominant species were M. aviumin the HIV/AIDS group and M. abscessus in the non-HIV/AIDS group. With respect to radiologic characteristics, different NTM strains were associated with distinct imaging manifestations; for example, M. marseillense, M. kansasii, and M. parasenchytosis were more likely to induce cavities. Imaging cavities, bronchiectasis, and acinar-like changes were more common in the non-HIV/AIDS groups.

CONCLUSIONS

The infection rates of HIV and NTM in Chongqing are high, while M. abscessus, M. paraintracellulare, and M. colombiense are the main pathogens causing NTM diseases in Chongqing, and NTM strains differed significantly between patients with and without HIV/AIDS. Monitoring these indicators can help develop prevention strategies.