Construction of a thoracic surgery database system and platform for regional information interactions on pulmonary tuberculosis_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

SONG Yijie ¹ , XU Xiuyuan ² , WANG Ning ^1,3 , JIA Shuang ¹ , YUAN Ye ¹ , JIANG Liangshuang ¹ ,  YAO Xiaojun ^1,3

1. Department of Thoracic Surgery, Public Health Clinical Center of Chengdu, Chengdu, 610061, P. R. China;
2. College of Computer Science, Sichuan University, Chengdu, 610065, P. R. China;
3. School of Public Health, Chengdu Medical College, Chengdu, 610500, P. R. China;

Corresponding?author：

YAO Xiaojun, Email: flyingyao@163.com

Keywords：

Database; pulmonary tuberculosis; thoracic surgery; information share

DOI：

10.7507/1007-4848.202307005

Video：

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Objective To collect and store all interactions relating to medical information between our center and allied specialized hospitals by constructing a database system for thoracic surgery and pulmonary tuberculosis. Methods We collected all related medical records of patients who had been clinically diagnosed with pulmonary tuberculosis and tuberculous empyema using the CouchBase Database, including outpatient and inpatient system of the Department of Thoracic Surgery at the Public Health Clinical Center of Chengdu between January 2017 to June 2023. Then, we integrated all medical records derived from the radiology information system, hospital information system, image archiving and communication systems, and the laboratory information management system. Finally, we used artificial intelligence to generate a database system for the application of thoracic surgery on pulmonary tuberculosis, which stored structured medical data from different hospitals along with data collected from patients via WeChat users. The new database could share medical data between our center and allied hospitals by using a front-end processor. Results We finally included 124 patients with 86 males and 38 females aged 43 (26, 56) years. A structured database for the application of thoracic surgery on patients with pulmonary tuberculosis was successfully constructed. A follow-up list created by the database can help outpatient doctors to complete follow-up tasks on time. All structured data can be downloaded in the form of Microsoft Excel files to meet the needs of different clinical researchers. Conclusion Our new database allows medical data to be structured, stored and shared between our center and allied hospitals. The database represents a powerful platform for interactions relating to regional information concerning pulmonary tuberculosis.

Citation： SONG Yijie, XU Xiuyuan, WANG Ning, JIA Shuang, YUAN Ye, JIANG Liangshuang, YAO Xiaojun. Construction of a thoracic surgery database system and platform for regional information interactions on pulmonary tuberculosis. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2023, 30(12): 1749-1756. doi: 10.7507/1007-4848.202307005 Copy

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2.	Borisov SE, D'Ambrosio L, Centis R, et al. Outcomes of patients with drug-resistant-tuberculosis treated with bedaquiline-containing regimens and undergoing adjunctive surgery. J Infect, 2019, 78(1): 35-39.
3.	Vashakidze SA, Gogishvili SG, Nikolaishvili KG, et al. Adjunctive surgery versus medical treatment among patients with cavitary multidrug-resistant tuberculosis. Eur J Cardiothorac Surg, 2021, 60(6): 1279-1285.
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11.	Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform, 2009, 42(2): 377-381.
12.	Allie T, Jackson A, Ambler J, et al. TBDBT: A TB database template for collection of harmonized TB clinical research data in REDCap, facilitating data standardization for inter-study comparison and meta-analyses. PLoS One, 2021, 16(3): e0249165.
13.	Willers C, Lynch T, Chand V, et al. A versatile, secure, and sustainable all-in-one biobank-registry data solution: The A3BC REDCap Model. Biopreserv Biobank, 2022, 20(3): 244-259.
14.	Pellison FC, Rijo R, Lima VC, et al. Data integration in the brazilian public health system for tuberculosis: Use of the semantic web to establish interoperability. JMIR Med Inform, 2020, 8(7): e17176.
15.	World Health Organization. Global tuberculosis report 2022. URL: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022. Accessed on 2023-06-28.
16.	陳留萍. 慢性病患者生命質量測定量表體系之肺結核患者生命質量測定量表QLICD-PT研制與應用. 昆明醫科大學, 2012.
17.	戴維, 王雅琴, 魏星, 等. 無紙化臨床研究數據采集平臺構建. 醫學信息學雜志, 2021, 42(4): 75-78.
18.	Ahmad N, Ahuja SD, Akkerman OW, et al. Treatment correlates of successful outcomes in pulmonary multidrug-resistant tuberculosis: An individual patient data meta-analysis. Lancet, 2018, 392(10150): 821-834.
19.	閆銀鎖, 陶荔瑩, 張紅偉, 等. 基于Power Pivot構建新病原學陽性肺結核患者耐藥篩查輔助監測系統. 結核病與肺部健康雜志, 2019, 8(4): 294-298.

1. Mirzayev F, Viney K, Linh NN, et al. World Health Organization recommendations on the treatment of drug-resistant tuberculosis, 2020 update. Eur Respir J, 2021, 57(6): 2003300.
2. Borisov SE, D'Ambrosio L, Centis R, et al. Outcomes of patients with drug-resistant-tuberculosis treated with bedaquiline-containing regimens and undergoing adjunctive surgery. J Infect, 2019, 78(1): 35-39.
3. Vashakidze SA, Gogishvili SG, Nikolaishvili KG, et al. Adjunctive surgery versus medical treatment among patients with cavitary multidrug-resistant tuberculosis. Eur J Cardiothorac Surg, 2021, 60(6): 1279-1285.
4. Fox GJ, Mitnick CD, Benedetti A, et al. Surgery as an adjunctive treatment for multidrug-resistant tuberculosis: An individual patient data metaanalysis. Clin Infect Dis, 2016, 62(7): 887-895.
5. 賈霜, 宋毅杰, 吳邦貴, 等. 胸腔鏡胸膜剝脫術在治療Ⅲ期結核性膿胸中的臨床應用. 中華結核和呼吸雜志, 2023, 46(5): 474-479.
6. Tang J, Tang Z, Feng C, et al. Efficacy and safety of video-assisted thoracoscopic surgery for pulmonary TB. Int J Tuberc Lung Dis, 2023, 27(5): 387-394.
7. 宋言崢, 朱益軍, 陳輝, 等. 肺結核外科的微創傷療法(附39例報告). 中華胸部外科電子雜志, 2020, 7(2): 71-75.
8. 蔣鈺輝, 申磊, 戴希勇. 單向式肺葉切除術在復雜肺結核手術中應用的回顧性隊列研究. 中國胸心血管外科臨床雜志, 2022, 29(12): 1607-1611.
9. Shaffer JG, Doumbia SO, Ndiaye D, et al. Development of a data collection and management system in West Africa: Challenges and sustainability. Infect Dis Poverty, 2018, 7(1): 125 .
10. 常煒, 車勇, 關小軍. 單中心肺結核外科數據庫的建立與應用. 結核病與肺部健康雜志, 2017, 6(1): 77-81.
11. Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform, 2009, 42(2): 377-381.
12. Allie T, Jackson A, Ambler J, et al. TBDBT: A TB database template for collection of harmonized TB clinical research data in REDCap, facilitating data standardization for inter-study comparison and meta-analyses. PLoS One, 2021, 16(3): e0249165.
13. Willers C, Lynch T, Chand V, et al. A versatile, secure, and sustainable all-in-one biobank-registry data solution: The A3BC REDCap Model. Biopreserv Biobank, 2022, 20(3): 244-259.
14. Pellison FC, Rijo R, Lima VC, et al. Data integration in the brazilian public health system for tuberculosis: Use of the semantic web to establish interoperability. JMIR Med Inform, 2020, 8(7): e17176.
15. World Health Organization. Global tuberculosis report 2022. URL: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022. Accessed on 2023-06-28.
16. 陳留萍. 慢性病患者生命質量測定量表體系之肺結核患者生命質量測定量表QLICD-PT研制與應用. 昆明醫科大學, 2012.
17. 戴維, 王雅琴, 魏星, 等. 無紙化臨床研究數據采集平臺構建. 醫學信息學雜志, 2021, 42(4): 75-78.
18. Ahmad N, Ahuja SD, Akkerman OW, et al. Treatment correlates of successful outcomes in pulmonary multidrug-resistant tuberculosis: An individual patient data meta-analysis. Lancet, 2018, 392(10150): 821-834.
19. 閆銀鎖, 陶荔瑩, 張紅偉, 等. 基于Power Pivot構建新病原學陽性肺結核患者耐藥篩查輔助監測系統. 結核病與肺部健康雜志, 2019, 8(4): 294-298.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Construction of a thoracic surgery database system and platform for regional information interactions on pulmonary tuberculosis

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