Evidence map›Paper›PMID 40301766›Full record

ArticleBMC infectious diseases2025

Clinical characteristics and prognostic factors of pulmonary tuberculosis with interstitial changes.

Junjie Ning, Shenglin Chi, Yuanwei Zhang, Lina Qiao

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Junjie NingPediatric Intensive Care Unit, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
Shenglin ChiDepartment of tuberculosis, First People's Hospital, Zigong City, Zigong, Sichuan Province, China.
Yuanwei ZhangDepartment of tuberculosis, First People's Hospital, Zigong City, Zigong, Sichuan Province, China.
Lina QiaoPediatric Intensive Care Unit, West China Second University Hospital, Sichuan University, Chengdu, 610041, China. qiaolina@scu.edu.cn.

Funding

National Key Research and Development Program of China 2021YFC2701700, 2021YFC2701704
6 · The paper itself

Abstract

backgroundPulmonary tuberculosis (PTB) remains a significant global public health challenge, particularly in its manifestation as interstitial lung disease. This form complicates clinical presentation, increasing the difficulty of diagnosis and treatment. However, studies on PTB with interstitial changes are relatively scarce, and their clinical significance and prognostic value have not been fully explored. The objective of the present study was to identify the key factors affecting clinical characteristics and prognosis in these patients.

methodsThis retrospective study analyzed data from patients diagnosed with PTB with Interstitial Changes at Zigong First People's Hospital in Sichuan Province between January 2014 and January 2024. Sixteen patients meeting strict inclusion and exclusion criteria were enrolled. Clinical characteristics and key prognostic factors were identified using descriptive statistics and random forest analysis, with partial dependence plots generated to illustrate the independent contributions of each variable to adverse outcomes.

resultsAmong the 16 patients studied, 75.0% were male and 25.0% were female. The average number of pathogen species detected was 1.56 ± 0.73, and 31.3% of patients presented with fever symptoms at admission. Hospital stay durations ranged from 8 to 67 days, with a mean of 22.00 ± 16.02 days. Regarding drug resistance, 25.0% of patients exhibited rifampicin resistance, and approximately 31.2% had underlying diseases. Ultimately, 11 patients (68.8%) recovered, while 5 (31.2%) died. The random forest model identified age, rifampicin resistance, and the number of pathogen species as the main determinants of prognosis. Advanced age and drug resistance were significantly associated with a higher risk of death, and patients infected with multiple pathogens experienced worse outcomes.

conclusionsThis study enhances our understanding of the clinical characteristics and prognosis of tuberculosis patients presenting with interstitial lung disease, particularly identifying advanced age, rifampicin resistance, and a higher number of pathogen species as key prognostic factors. These findings provide valuable insights for the development of personalized treatment strategies and precision medicine approaches for this patient group.

Indexed as

Lung Diseases, InterstitialTuberculosis, PulmonaryAdultAgedAntitubercular AgentsChinaFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesRifampinAntitubercular AgentsRifampinAgeDrug resistanceInterstitial lung diseasePrognosisPulmonary tuberculosisRandom forest model

Identifiers

PMID40301766
PMCPMC12039248

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.