Table of Content

10 March 2024, Volume 46 Issue 3

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Editorial

Promoting the development and application of artificial intelligence technology in the field of pulmonary tuberculosis imaging

Li Duo, Lyu Pingxin

Chinese Journal of Antituberculosis. 2024, 46(3):  253-259.  doi:10.19982/j.issn.1000-6621.20240013

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Interpretation of Standards

Interpretation of the World Health Organization’s Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance (2nd Edition)

Pei Shaojun, Ou Xichao

Chinese Journal of Antituberculosis. 2024, 46(3):  260-266.  doi:10.19982/j.issn.1000-6621.20230450

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Drug-resistant tuberculosis is a major obstacle to the achievement of the End TB Strategy by 2035. The World Health Organization (WHO) promotes the use of rapid molecular drug susceptibility testing technology for early diagnosis of drug-resistant tuberculosis. Covering comprehensive and reliable drug resistance-related mutations is the key to improving the reliability of molecular drug resistance diagnosis. WHO has released the second edition of the Catalogue of mutations in Mycobacterium tuberculosis complex and their association with drug resistance in November 2023. This catalogue described more comprehensive and accurate drug-resistance-related gene mutations based on the largest collection of multinational Mycobacterium tuberculosis complex isolates, so as to support the development and improvement of the molecular drug susceptibility testing technology. This article provides a detailed interpretation of the updates in the analysis process and drug-resistant mutations in the second edition of the catalogue compared with the first edition, and looks forward to the future direction of improving the catalogue.

Special Topic

Thoughts on improving the tuberculosis screening policy in diabetes patients in China

Zhu Limei

Chinese Journal of Antituberculosis. 2024, 46(3):  267-271.  doi:10.19982/j.issn.1000-6621.20230436

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Diabetes not only increases the risk of tuberculosis, but also increases treatment failure, recurrence and death in tuberculosis patients. Based on the existing studies, the large-scale tuberculosis screening among diabetes patients in China had low yields. It may be more cost-effective to carry out tuberculosis screening in high-burden areas or diabetes patients with high-risk factors. In this paper, the author pointed out the existing problems of tuberculosis screening in diabetes patients in China, and pointed out the corresponding plans and suggestions.

Original Articles

Deep learning to determine the healing status of pulmonary tuberculosis lesions on CT images

Qin Liyi, Lyu Pingxin, Guo Lin, Qian Lingjun, Xiao Qian, Yang Yang, Shang Yuanyuan, Jia Junnan, Chu Naihui, Liu Yuanming, Li Weimin

Chinese Journal of Antituberculosis. 2024, 46(3):  272-278.  doi:10.19982/j.issn.1000-6621.20230457

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Objective: To construct a deep learning model based on CT images for activity assessment of pulmonary tuberculosis lesions. Methods: A retrospective cohort of 102 cured pulmonary tuberculosis patients at Beijing Chest Hospital, Capital Medical University between December 2018 and December 2020 was included, CT data were collected before, during, and after treatment. Lesions were randomly divided into training and test sets with an 8∶2 ratio. Additionally, a prospective cohort of 72 cured pulmonary tuberculosis patients was enrolled between October 2021 and December 2022, CT datasets were collected for an independent validation set. A deep learning model was constructed through transfer learning using the Mask R-CNN architecture to achieve automatic lesion segmentation and activity determination. The model was trained based on three-dimensional lesion labels from the training set, and its performance in determining the activity of pulmonary tuberculosis lesions was evaluated in the test set and independent validation set by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: A retrospective cohort of 102 cured pulmonary tuberculosis patients who met the criteria was included, and a total of 770 CT imaging data were collected; 332 lesions were active, and 464 lesions were inactive. A prospective cohort of 72 cured patients with pulmonary tuberculosis was included, and a total of 540 CT imaging data were collected. The transfer learning-based Mask R-CNN deep learning model achieved an AUC of 87.5%, sensitivity of 85.7%, and specificity of 78.6% in the test set. In the independent validation set, the model obtained an AUC of 79.9%, sensitivity of 78.7%, and specificity of 75.0%. Conclusion: The transfer learning-based Mask R-CNN deep learning model has shown promising potential in predicting the activity of small-scale pulmonary tuberculosis lesions, could offer valuable scientific insights for rapid and automatic clinical decision-making.

Construction and evaluation of a CT-based deep learning model for the auxiliary diagnosis of secondary tuberculosis

Liu Xueyan, Wang Fang, Li Chunhua, Tang Guangxiao, Zheng Jiaofeng, Wang Huiqiu, Li Yurui, Wang Jia’nan, Shu Weiqiang, Lyu Shengxiu

Chinese Journal of Antituberculosis. 2024, 46(3):  279-287.  doi:10.19982/j.issn.1000-6621.20230356

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Objective: To develop a deep learning-based auxiliary diagnostic model for secondary tuberculosis using CT scans and evaluate its clinical applicability. Methods: A retrospective collection was conducted on clinical data of 2004 patients who underwent chest CT scans at the Chongqing Public Health Medical Center from December 2018 to April 2023. The patients were divided into three groups: secondary tuberculosis (934 patients), ordinary lung infection (526 patients), and normal lungs (544 patients). Using a completely random sampling method, the dataset was divided into a training set (1402 patients, 70.0%) and a test set (602 patients, 30.0%). An automatic lung field segmentation algorithm was applied to isolate the lung field in all images. BasicNet and DenseNet classification algorithms were used for categorize the three groups. The discriminative performance of the model was evaluated using metrics such as area under curve (AUC), sensitivity, specificity, and accuracy. Finally, the optimal model was compared with three radiologists of different years of experience using testing data. Results: Using 602 samples in an independent test set, the DenseNet model demonstrated superior performance compared to the BasicNet model. They achieved an average AUC, sensitivity, specificity, and accuracy of 92.1% vs. 89.4%, 79.7% vs. 74.0%, 89.4% vs. 86.6%, and 86.2% vs. 83.3%, respectively. The diagnostic performance of the DenseNet model was superior to that of young doctors (accuracy: 90.7% and 89.1%, Kappa=0.677) and exhibited high diagnostic consistency with middle and highly experienced radiologists without any significant difference (accuracy: 90.7%, 92.2% and 95.3%, Kappa=0.746, 0.819). Conclusion: The DenseNet model can accurately identify secondary tuberculosis, achieving a competency level similar to a middle experienced radiologist, making it a potential auxiliary diagnostic tool for secondary tuberculosis.

Comparison of the performance of deep learning models ResNet18 and ResNet50 based on multiphase CT for the diagnosis of renal tuberculosis

Yi Wanqing, Zheng Xueyi, Zhang Zhuang, Sun Weirong, Yuan Xiaodong

Chinese Journal of Antituberculosis. 2024, 46(3):  288-293.  doi:10.19982/j.issn.1000-6621.20230375

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Objective: To investigate the feasibility of deep learning models based on CT images for the differential diagnosis of renal tuberculosis. Methods: A retrospective analysis was conducted on 200 patients (400 kidneys) admitted to the Eighth Medical Center of the General Hospital of the PLA from September 2018 to August 2020, diagnosed with renal tuberculosis, renal tumors, pyelonephritis, normal kidneys, renal cysts, or hydronephrosis by pathological or clinical confirmation. The 400 CT images of the kidneys were divided into the tuberculosis group (n=114) and the non-tuberculosis group (n=286), and then further divided into a training set (renal tuberculosis: 85; non-renal tuberculosis: 235) and a test set (renal tuberculosis: 29; non-renal tuberculosis: 51) with the ratio of 8∶2. Deep learning models for the unenhanced phase, corticomedullary phase, nephrographic phase, and excretory phase of the kidneys were constructed using the ResNet18 and ResNet50 networks based on the training set. The diagnostic performance of the constructed models for renal tuberculosis was evaluated based on the test set, including the calculation of the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F₁ score. Results: In the training set, the average age of the tuberculosis group ((41.27±11.75) years) was lower than that of the non-tuberculosis group ((54.05±13.97) years), with a statistically significant difference (t=5.753, P<0.05). In the test set, the average age of the tuberculosis group ((44.06±11.95) years) was significantly lower than that of the non-tuberculosis group ((56.12±10.73) years)(t=3.444, P<0.05). In the training set, males accounted for 66.7% (40/60) and females accounted for 33.3% (20/60) in the tuberculosis group, while in the non-tuberculosis group, males accounted for 60.9% (78/128) and females accounted for 39.1% (50/128); however, the gender distribution showed no statistically significant difference in the training set (χ²=0.009, P=0.924). In the test set, 64.3% (18/28) of individuals in the tuberculosis group were male, and 35.7% (10/28) were female; in the non-tuberculosis group, 58.7% (27/46) were male, and 41.3% (19/46) were female, with no significant difference (χ²=0.018, P=0.894). The AUC, sensitivity, specificity, accuracy, and F₁ score of the four-phase images were all higher in the ResNet18 model compared to those in the ResNet50 model. The ResNet18 model demonstrated superior performance in the corticomedullary phase, with an AUC of 0.925 and corresponding sensitivity, specificity, accuracy, and F₁ score of 93.1%, 86.3%, 88.7%, and 0.857, respectively. In contrast, the AUC for the medullary phase of the ResNet50 model was 0.858, with corresponding sensitivity, specificity, accuracy, and F₁ score of 72.4%, 84.3%, 80.0%, and 0.724, respectively. Conclusion: The diagnostic performance of the ResNet18 model for renal tuberculosis based on multi-phase CT images was superior to that of the ResNet50 model. And the corticomedullary phase exhibited the best diagnostic performance in the ResNet18 model, indicating the high clinical application value.

Model construction and validation for predicting active drug-resistant pulmonary tuberculosis using combined CT radiomics and clinical features

Pan Ben, Liang Changhua, Han Dongming, Cui Junwei, Yao Yangyang, Wei Zhengqi, Zhen Siyu, Wei Hanyu, Yang Xinmiao

Chinese Journal of Antituberculosis. 2024, 46(3):  294-301.  doi:10.19982/j.issn.1000-6621.20230278

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Objective: To construct a model based on CT imaging radiomics combined with clinical features to predict the drug resistance of active pulmonary tuberculosis. Methods: The study included 234 patients with pulmonary tuberculosis admitted to The First Affiliated Hospital of Xinxiang Medical University from January 1, 2020 to December 31, 2022. Based on drug resistance status, the patients were divided into two groups: 88 cases in the drug-resistant group and 146 cases in the drug-sensitive group. They were then randomly assigned to a training set and a testing set in a ratio of 7∶3.Volume of interest (VOI) delineation was performed on the lesions, and radiomics features were extracted. Feature selection was conducted using the minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) methods. Logistic regression was employed to establish clinical models and radiomics models separately. Subsequently, the selected optimal radiomics features, statistically significant clinical features, and CT features were combined to construct a joint model. The diagnostic performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Among the patients with drug resistance, there were 48 primary cases (54.55%) and 40 retreatment cases (45.45%). The detection rate of tree-in-bud sign was 69.32% (61/88). In the drug-sensitive group, there were 131 primary cases (89.73%) and 15 retreatment cases (10.27%). The detection rate of tree-in-bud sign was 81.51% (119/146). The clinical and CT feature analysis of patients in the drug-resistant and drug-sensitive group showed that there were statistically significant differences in treatment history (χ²=37.796, P<0.001) and tree-in-bud sign (χ²=4.595, P=0.032) between the two groups. Regarding CT findings analysis, the interobserver agreements between two physicians were good for the observation of nodules and satellite lesions, calcified nodules, consolidation, fibrotic bands, bronchial dilation, and tree-in-bud sign (Kappa coefficients were 0.757, 0.784, 0.818, 0.777, 0.863, and 0.781, respectively). A total of 14 radiomics features were selected as predictive indicators using the MRMR and LASSO methods to construct the prediction model. The AUC of the clinical model in the training set and testing set were 0.760 (95%CI: 0.687-0.834) and 0.820 (95%CI: 0.704-0.937), respectively. The AUC of the radiomics model in the training set and testing set were 0.822 (95%CI: 0.758-0.885)and 0.845 (95%CI: 0.744-0.947), respectively. The AUC of the combined model in the training set and testing set were 0.878 (95%CI: 0.823-0.932) and 0.888 (95%CI: 0.788-0.987), respectively. Conclusion: The radiomics model exhibited higher diagnostic performance than the clinical model, while the combined model showed the best diagnostic performance in both the training and testing sets.

Differentiation of pulmonary tuberculosis and nontuberculous mycobacterial pulmonary disease based on computed tomography radiomics combined with clinical features

Yao Yangyang, Liang Changhua, Han Dongming, Cui Junwei, Pan Ben, Wang Huihui, Wei Zhengqi, Zhen Siyu, Wei Hanyu

Chinese Journal of Antituberculosis. 2024, 46(3):  302-310.  doi:10.19982/j.issn.1000-6621.20230337

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Objective: To explore the value of combining CT-based radiomics with clinical features in distinguishing pulmonary tuberculosis (PTB) from nontuberculous mycobacteria pulmonary disease (NTM-PD). Methods: A retrospective analysis was conducted on clinical data and CT images of NTM-PD and PTB patients confirmed with culture from The First Affiliated Hospital of Xinxiang Medical University from January 1, 2019, to March 31, 2023. Based on the results of bacterial culture, all patients were divided into the PTB group (58 cases) and the NTM-PD group (75 cases). Clinical features were analyzed, and statistically significant features were used to construct a clinical model. CT images were used to study cavitary lesions, with a total of 200 lesions included in the study. The lesions were randomly divided into a training set and a testing set in a 7∶3 ratio. A logistic regression classifier was used to construct a radiomics model. A combined model was built by integrating radiomics features and clinical features. The diagnostic performance of the models in the training and testing sets was evaluated by sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration curve. Results: Univariate analysis showed that there was statistically significant difference in age between the PTB group (median (quartile) age 45 (26, 66) years) and the NTM-PD group (median (quartile) age 63 (54, 70) years)(Z=-3.184, P<0.001). There was also statistically significant difference in BMI between the PTB group (19.95±2.83) and the NTM-PD group (18.78±2.59)(t=2.469, P=0.015). The proportions of patients with positive interferon-gamma release assays (IGRA) results were significantly different between the PTB group (55 cases, 73.33%) and the NTM-PD group (16 cases, 27.59%)(χ²=27.505, P<0.001). Multivariate analysis showed that age (OR=0.969, P=0.004) and IGRA (OR=6.026, P<0.001) were independent predictive factors for distinguishing PTB from NTM-PD. The AUC values of the clinical model in the training and testing sets were 0.832 (95%CI: 0.765-0.899) and 0.800 (95%CI: 0.689-0.911), respectively. The AUC values of the radiomics model in the training and testing sets were 0.974 (95%CI: 0.952-0.996) and 0.939 (95%CI: 0.877-1.000), respectively. The AUC values of the combined model in the training and testing sets were 0.993 (95%CI: 0.986-1.000) and 0.995 (95%CI: 0.985-1.000), respectively. Conclusion: The combined model incorporating clinical features and radiomics features is a non-invasive, convenient, and rapid diagnostic method that can effectively distinguish PTB from NTM-PD.

Evaluation of the effectiveness of cerebrospinal fluid characteristics indicators in assisting rapid screening of tuberculous meningitis

Wang Chenyuan, Wang Shanshan, Wang Sainan, Shao Ge, Cao Jiayi, Xiong Haiyan, Hu Yi

Chinese Journal of Antituberculosis. 2024, 46(3):  311-317.  doi:10.19982/j.issn.1000-6621.20230419

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Objective: To explore the apllication of several cerebrospinal fluid (CSF) indicators to improve the detection sensitivity and achieve rapid screening and early treatment control of tuberculous meningitis (TBM). Methods: From 2011 to 2019, according to the inclusion and exclusion criteria, 100 TBM patients were randomly selected from 383 suspected TBM patients admitted to tuberculosis designated medical institutions in Suzhou of Jiangsu Province, Zigong of Sichuan Province and Guiyang of Guizhou Province. And 100 non-TBM patients were selected based on age and sex matched of 1∶1. The biochemical indicators such as white blood cell count, lactate, total protein, adenosine deaminase, and glucose in CSF were collected. Ultra high-throughput liquid chromatography was used to separate the metabolites of CSF. The metabolic indicators with statistically significant differences in distribution were screened by Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA). A classification and regression tree (boosted-CART) model was constructed by machine learning. The effect of characteristic indicators on rapid screening of TBM patients with various types was evaluated. Results: Using the criteria of P<0.05, OPLS-DA variable important in projection value >1 and absolute value of Forld Change >1, the above model was combined to screen CSF L-glutamine and glucose levels as characteristic indicators. The cutoff values were 607.06 mmol/L (L-glutamine) and 60.03 mg/dl (glucose), respectively. The combination of the above two indicators improved the sensitivity of detecting probable and possible TBM from 66.7% (95%CI: 24.1%-94.0%) to 83.3% (95%CI: 36.5%-99.1%),AUC increased from 0.667 (95%CI: 0.444-0.889) to 0.708 (95%CI: 0.494-0.923)(Z=0.261, P>0.05). Conclusion: Biochemical and metabolic indicators such as CSF L-glutamine and glucose can improve the sensitivity of rapid TBM screening, which is conducive to the early diagnosis and treatment of TBM and to control the spread of tuberculosis.

Analysis of tuberculosis prevalence and control along the China-Proposed Belt and Road Initiative

Shu Wei, Guo Yusun, Li Shanshan, Pang Yu, Liu Yuhong, Mei Yang

Chinese Journal of Antituberculosis. 2024, 46(3):  318-324.  doi:10.19982/j.issn.1000-6621.20230405

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Objective: To analyze current situation around tuberculosis (TB) control and prevention for counties along the “Belt and Road” in 2021, which includes information on TB epidemic, case finding, treatment outcomes et al. Methods: Of 151 countries that officially signed Belt and Road Initiative cooperation document with China were included in our study. Relevant data were collected from the Global Tuberculosis Report released by WHO in 2022. Data on TB incidence, mortality, case detection, and treatment outcome were analyzed. Results: The entire population of 151 countries was 3.691 billion, accounting for 46.84% of the global population. The total number of estimated TB cases of 151 countries in 2021 was 6.4570 million, which contributes to 60.92% of global burden. The incidence rate was 174.94 per 100000. The number of TB death among HIV-negative patients was 816.2 thousand (59.14% of global total). The estimated number of new cases with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) was 263.7 thousand (58.59% of global total). The number of HIV-related tuberculosis patients was 611.7 thousand and the death from HIV-positive TB cases was 165.1 thousand, which accounted for 87.01% and 88.32% of global total respectively. Majority of TB incident cases and deaths were in Asian and African countries and 8 of them belong to WHO defined high burden countries of TB, MDR/RR-TB, and TB/HIV. The overall case detection rate of the Belt and Road countries were 56.00% which was significantly lower than Chinese level of 75.04% and the global level of 60.70%. 63.00% of notified TB cases were diagnosed by laboratory tests, which was close to the global average (63.47%). Out of 151 countries, only 68 countries and 19 countries achieved ≥85% of treatment success rate for newly diagnosed or relapsed patients and retreated cases respectively. Conclusion: The experience exchange and cooperation mechanism for tuberculosis control and prevention between countries should be established on the platform of Belt and Road. It is needed to strengthen information sharing on infectious disease among countries, to build-up modern frontier inspection mechanism, and to constantly reduce the prevalence of tuberculosis.

Baseline survey and analysis of tuberculosis care pilot programme

Wang Yunxia, Meng Qinglin, Liu Eryong, Zhou Lin

Chinese Journal of Antituberculosis. 2024, 46(3):  325-332.  doi:10.19982/j.issn.1000-6621.20230359

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Objective: To analyze the tuberculosis prevention and control status of the first 40 pilot counties of the tuberculosis care action project before implementation, and provide baseline data for the implementation effect evaluation. Methods: From April to June 2023, the National Center for Tuberculosis Control and Prevention of China CDC issued a unified semi-structured electronic questionnaire to 40 pilot counties from 15 provinces which were the first batch to implement the “Tuberculosis Care Action Pilot Project”(hereinafter referred to as the “pilot project”), to understand and analyze the service capacity of tuberculosis designated medical institutions, and implementation of measures such as humanistic care for tuberculosis patients and screening for tuberculosis key populations in pilot counties before the start of the pilot project (in 2022). Results: Among the 40 pilot counties, tuberculosis designated medical institutions were mainly composed of comprehensive hospitals (23 (57.50%)), followed by specialized hospitals (8 (20.00%)) and centers for disease control and prevention (6 (15.00%)). There were total 1017 tuberculosis prevention and control personnel in designated medical institutions of 40 counties (with a median of 19.5 (12.5, 34.0) each, range 3 to 64), mainly composed of outpatient and resident doctors (326 (32.05%)), followed by nursing personnel (225 (22.12%)), imaging doctors and laboratory personnel (187 (18.39%) and 134 (13.18%) respectively). A total of 10123 tuberculosis patients were reported, including 9980 patients (98.59%) of pulmonary tuberculosis (including 6326 (63.39%) etiology positive patients) and 143 patients (1.41%) of extrapulmonary tuberculosis. The proportion of etiology negative pulmonary tuberculosis meeting clinical diagnosis standard was 85.69% (629/734) overall, however, the proportion of etiology negative tracheobronchial tuberculosis meeting clinical diagnosis standard was only 17.86% (5/28). Forty pilot counties all conducted acid fast bacterial smear microscopy and PPD tests. Among them, 39 (97.50%), 37 (92.50%), and 32 (80.00%) counties conducted Mycobacterium culture, Mycobacterium tuberculosis nucleic acid testing, and anti tuberculosis drug resistance screening, respectively. However, only 16 (40.00%) and 19 (47.50%) pilot counties conducted Mycobacterium tuberculosis fusion protein test and interferon gamma release assay (IGRA), respectively. Thirty-nine (97.50%), 31 (77.50%), 14 (35.00%), and 23 (57.50%) counties carried out X-ray photography, CT examination, artificial intelligence film reading technology, and remote diagnosis, respectively. Seventeen (42.50%) counties applied information technology to manage patients, and 15 (37.50%) counties provided transportation or nutrition subsidies for patients. Only less than 30% counties could provide service such as nutrition assessments, nutrition supportive treatments, and psychological support for tuberculosis patients; tuberculosis was included in the outpatient special diseases list in 36 (90.00%) counties, and drug-resistant tuberculosis was included in the major disease security list in 26 (65.00%) counties; and 26 counties (65.00%) provided reimbursement for tuberculosis molecular biological testing. The overall screening rate of chest imaging of HIV/AIDS patients and close contacts of tuberculosis patients were high (96.06% (17105/17807) and 92.81% (46884/50515) respectively), but the overall screening rate for elderly people aged 65 and above and diabetes patients was low (13.48% (377436/2800877) and 12.27% (93808/764416) respectively). The implementation rate of tuberculosis screening for freshmen in school among pilot counties was not high (60.00% (24/40)-80.00% (32/40)), and that for nurseries and welfare institutions was also low (20.69% (6/29) and 25.00% (8/32) respectively). Conclusion: All pilot counties have already had the basic hardware conditions required for tuberculosis diagnosis before the pilot project, but they still need to be further improved in the diagnosis ability of etiology negative pulmonary tuberculosis, the application of information management methods, the implementation of patient humanistic care measures and medical insurance policies, and the screening quality of tuberculosis key populations.

Evaluation of health economics of implementation of tuberculosis prevention and control program in Sichuan Province from 2011 to 2020

Li Ting, Liu Shuang, Wang Danxia, Lu Jia, Cheng Qianqian, Chen Chuang, He Jin’ge, Zhang Linglin, Xia Yong, Li Jing, Zhang Shu, Gao Wenfeng, Xia Lan

Chinese Journal of Antituberculosis. 2024, 46(3):  333-339.  doi:10.19982/j.issn.1000-6621.20230397

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Objective: To evaluate the social benefits generated by the funding for tuberculosis prevention and control from 2011 to 2020 in Sichuan Province, and to provide evidence for the development of medium- and long-term development plans for the new era. Methods: Retrospective study method was used to collect the treatment outcomes of smear positive pulmonary tuberculosis patients and the proportion of working population among pulmonary tuberculosis patients from 2011 to 2020 (during the 12th Five-Year and the 13th Five-Year plan of National Tuberculosis and Control Program (NTP)) in Sichuan Province from the Tuberculosis Management Information System and the Monitoring of Infections Diseases System, and to collect funding data and gross domestic product (GDP) from the Sichuan Statistical Yearbook and the self-assessment questionnaire of the final evaluation of NTP implementation. The social effect, social utility and social benefit of tuberculosis prevention and control funds investment in the past 10 years were estimated using three analysis methods commonly used in health economics: cost-effectiveness, cost-utibility and cost-benefit. At the same time, the estimated results of the 12th Five-Year Plan and the 13th Five-Year Plan were compared and analyzed. Results: From 2011 to 2020, 760.326 million yuan were invested for tuberculosis prevention and control in Sichuan Province, and 160167 smear positive pulmonary tuberculosis patients were successfully treated, which prevented 696899 healthy individuals from tuberculosis infection and 1155277 disability adjusted life years (DALY) were saved. It saved 605 million yuan of medical expenses and 37.032 billion yuan of social and economic losses. 1091.01 yuan were needed to avoid a healthy person to be infected,and 658.13 yuan needed to save 1 DALY. Additional investment of 1 yuan can produce 49.50 yuan of economic benefits. The total social benefits (20.922 billion yuan) brought by the 13th Five-Year Plan increased by 25.18% ((209.22-167.14)/167.14) compared with the 12th Five-Year Plan (16.714 billion yuan), but the social benefits (47.56 yuan) brought by the government investment of one yuan for tuberculosis prevention and control decreased by 4.61 yuan compared with that of 52.17 yuan during the 12th Five-Year Plan period. Conclusion: From 2011 to 2020, the investment of governments at all levels and international projects invested in tuberculosis prevention and control in Sichuan has produced good social effects, effectiveness and benefits. It is recommended that, while maintaining the current level of funding, governments at all levels continue to increase their investment, especially in areas of case detection and treatment management of pathogen positive patients in order to achieve higher social benefits.

Analysis of drug resistance situation results and rifampicin resistance characteristics in pulmonary tuberculosis patients in Tianshui City from 2015 to 2022

Li Jianghong, Lei Caiying, Yan Shuping, Liu Xiaolan, Yang Qi, Wang Reqin, Liu Fang, Yang Shumin

Chinese Journal of Antituberculosis. 2024, 46(3):  340-348.  doi:10.19982/j.issn.1000-6621.20230366

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Objective: Analyze the drug resistance situation and basic characteristics of rifampicin resistance in pulmonary tuberculosis (PTB) patients from 2015 to 2022 in Tianshui City, Gansu Province, to provide scientific basis for optimizing the health policy of drug-resistant tuberculosis. Methods: Adopting a retrospective analysis method, we collected information of tuberculosis registration, laboratory testing, drug resistance screening and drug resistant tuberculosis diagnosis in Tianshui City from 2015 to 2022 from “China Disease Prevention and Control Information System” subsystem “Tuberculosis Management Information System”, analyzed etiologically positive rate among patients, drug susceptibility test results and resistance diagnosis time, etc. Results: From 2015 to 2022, a total of 8458 registered PTB patients were detected in Tianshui City, excluding tuberculous pleurisy, there were 7895 cases of PTB, with an etiologically positive rate of 28.32% (2236/7895), rising from 11.33% (177/1562) in 2015 to 61.30% (236/385) in 2022, showing an upward trend ({\chi }_{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{d}}^2=1014.480, P=0.000). Among 2360 patients with PTB who should be screened for drug resistance in Tianshui City, the drug resistance screening rate was 85.00% (2006/2360), which increased from 54.80% (97/177) in 2015 to 93.39% (240/257) in 2022, showing an upward trend({\chi }_{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{d}}^2=397.292, P=0.000), and the detection rate of drug resistance in Tianshui City was 98.90% (1984/2006), and the rifampicin resistance rate was 15.73% (312/1984), which increased from 10.42% (10/96) in 2015 to 28.57% (62/217) in 2017 and then decreased to 11.34% (27/238) in 2022, showing a trend of firstly increasing and then decreasing({\chi }_{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{d}}^2=27.248, P=0.000). Among the 312 patients with rifampicin resistance, there were more males (198 cases, 63.46%) than females (114 cases, 36.54%), relatively concentrated among 20-29 years old (85 cases, 27.24%) and 40-49 years old (66 cases, 21.15%) patients, mainly farmers (213 cases, 68.27%), and sputum smear results were mostly positive (215 cases, 68.91%). The proportion of elderly patients (60-83 years old) increased from 9.52% (2/21) in 2016 to 25.93% (7/27) in 2022, showing a clear upward trend ({\chi }_{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{d}}^2=4.801, P=0.028). The sputum smear positive rate decreased from 100.00% (10/10) in 2015 to 59.26% (16/27) in 2022, showing a downward trend({\chi }_{\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{d}}^2=17.664, P=0.000). The top three drug resistance profiles were RFP+INH+EMB (26.92%, 84/312), RFP (26.28%, 82/312), and RFP+INH (23.40%, 73/312). The median (quartile) diagnosis time for drug-resistant patients had decreased year by year from 145 (91, 196) days in 2016 to 21 (12, 39) days in 2019. Conclusion: The etiologically positive rate and drug resistance screening rate of PTB patients in Tianshui City had been increasing year by year from 2015 to 2022, the detection rate of rifampicin resistance fluctuated greatly, the time for drug resistance diagnosis had been significantly shortened. It is suggested to increase efforts for screening rifampicin resistance in elderly PTB patients to reduce spread of drug-resistant tuberculosis.

Influence of meteorological factors on the incidence of pulmonary tuberculosis in Hefei City from 2013 to 2022

You Enqing, Li Juan, Chen Lili, Liu Wei, Wu Jinju, Cao Hong

Chinese Journal of Antituberculosis. 2024, 46(3):  349-356.  doi:10.19982/j.issn.1000-6621.20230408

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Objective: The influence of meteorological factors on the incidence of pulmonary tuberculosis(PTB) in Hefei was analyzed to provide the basis for developing tuberculosis control strategies. Methods: The weekly number of cases of PTB in Hefei during 2013-2022 was extracted from the Chinese Disease Prevention and Control Information System, and the meteorological data during the same period were collected from the Hefei Meteorological Bureau. Spearman correlation analysis was used to study their association. A Distributed lag nonlinear model (DLNM) was established using R 4.3.0 software to investigate the exposure-lag effect between meteorological factors and the incidence of PTB. Results: A total of 41366 cases of PTB were reported in Hefei from 2013 to 2022, the reported incidence decreased from 63.2/100000 (4742/7506266) in 2013 to 31.4/100000 (2960/9424437) in 2022, showing a fluctuating decreasing trend ({\chi }_{\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{a}\mathrm{r}}^2=1622.439, P<0.001). The effects of temperature, relative humidity and wind speed on the incidence of pulmonary tuberculosis were “M” type, inverted “N” type and approximately “Z” type, respectively. The cumulative effect of temperature at 4.7℃ was the highest (cumulative relative risk (CRR)=2.261,95%CI:1.422-3.594). Low temperature (P₅=2.4℃) induced highest risk of PTB incidence at lag of 16 weeks. The cumulative effect of relative humidity at 46.1% was the highest (CRR=8.666,95%CI:5.452-13.773).Low relative humidity (P₁=54.7%) had the maximum RR at lag of 0 weeks (RR=1.073,95%CI:1.047-1.100). High relative humidity (P₉₉=93.0%) was a protective factor for PTB at lag of 0~15 weeks. The cumulative effect of wind speed on PTB was the highest at 1.2 m/s (CRR=1.563,95%CI:1.203-2.031).Low wind speed (P₁=1.2 m/s) had the maximum RR at lag of 16 weeks (RR=1.042,95%CI:1.011-1.073), and high wind speed (P₉₉=3.5m/s) was a protective factor for PTB at lag of 0-13 weeks. Conclusion: Meteorological factors play an important role in the incidence of PTB, and have hysteretic and non-linear effects. Low temperature, low relative humidity and low wind speed will increase the risk of PTB.

Analysis of risk factors of respiratory failure in elderly patients with pulmonary tuberculosis

Zhou Xue, He Fang, Di Yicheng, Zhang Zheng, Liu Ju

Chinese Journal of Antituberculosis. 2024, 46(3):  357-361.  doi:10.19982/j.issn.1000-6621.20230447

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Objective: To analyze the situation and risk factors of respiratory failure in elderly patients with pulmonary tuberculosis. Methods: Using a retrospective research method, 210 elderly pulmonary tuberculosis patients admitted to Liyang People’s Hospital in Jiangsu Province from January 2021 to June 2023 were selected as the study subjects. Among them, 100 cases with concurrent respiratory failure were selected as the observation group; 110 cases without concurrent respiratory failure were selected as the control group. Clinical data of the two groups were collected and compared, including age, gender, disease course, body mass index, smoking history, previous pulmonary disease history, classification of tuberculosis treatment, main clinical symptoms, laboratory test results, imaging test results, etc. Logistic regression model was used to analyze the risk factors of respiratory failure in elderly patients with pulmonary tuberculosis. Results: The proportion of retreated patients in the observation group was 47.0% (47/100), significantly higher than that in the control group’s 29.1% (32/110)(χ²=7.159, P=0.007); the levels of white blood cell count and procalcitonin were (11.50±2.63)×10⁹/L and (0.71±0.25) μg/L, significantly higher than those in the control group ((4.62±2.41)×10⁹/L and (0.29±0.17) μg/L), the differences were statistically significant (t values were 19.782 and 14.347, respectively, both P<0.001). The serum albumin level in the control group was (31.37±7.56) g/L, significantly higher than that in the observation group ((24.61±6.72) g/L) (t=6.821, P<0.001). Multivariate logistic regression analysis showed that retreated pulmonary tuberculosis (OR(95%CI)=1.262 (1.151-1.968)), elevated serum white blood cell count levels (OR(95%CI)=2.268(1.278-3.343)), elevated procalcitonin levels (OR(95%CI)=1.751(1.408-3.513)), and decreased serum albumin levels (OR(95%CI)=0.893(0.762-0.969)) were independent risk factors for respiratory failure in elderly pulmonary tuberculosis patients. Conclusion: Retreated pulmonary tuberculosis, increased white blood cell count and procalcitonin levels, and decreased serum albumin levels are independent risk factors for respiratory failure in elderly patients with pulmonary tuberculosis. Clinical physicians should be vigilant against these factors and take effective treatment measures to reduce the incidence of respiratory failure in elderly patients with pulmonary tuberculosis.

Review Articles

Research progress on imaging manifestations of nontuberculosis mycobacterial pulmonary and application of new artificial intelligence technology

Gao Shan, Nie Wenjuan, Hou Dailun, Chu Naihui

Chinese Journal of Antituberculosis. 2024, 46(3):  362-366.  doi:10.19982/j.issn.1000-6621.20230404

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The incidence of diseases caused by nontuberculosis mycobacterial is gradually increasing in the world, and it has become one of the key issues of tuberculosis prevention and control in China. Medical imaging has become an essential auxiliary diagnostic method due to the characteristic imaging manifestations of nontuberculosis mycobacteria pulmonary disease. Artificial intelligence has developed rapidly in the field of medicine, and imaging-based artificial intelligence technology has made new research attempts in the direction of nontuberculosis mycobacterial pulmonary disease in recent years. Therefore, the authors collates the imaging manifestations of non-tuberculosis mycobacteria pulmonary disease and introduces the application of new artificial intelligence technologies in imaging diagnosis, prognosis, and treatment evaluation, with the expectation of better understanding the development of disease in the field of artificial intelligence as well as providing new thoughts on the way of diagnosis and treatment evaluation.