Evidence map›Paper›PMID 41126090›Full record

ArticleBMC infectious diseases2025

Pre-treatment blood cell counts and inflammatory markers predict the radiologic severity of post-tuberculosis lung disease.

Yue Zhang, Xiaoyan Gai, Yafei Rao, Jingge Qu, Zikang Sheng, Danyang Li, Yuqiang Pei, Zihan Wang, Yu Pang, Mengqiu Gao and 2 more

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Circulating antibody signatures againstFrontiers in immunology · 2026
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yue Zhang *Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Xiaoyan Gai *Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Yafei RaoDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Jingge QuDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Zikang ShengDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Danyang LiDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Yuqiang PeiDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Zihan WangDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Yu PangDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China.
Mengqiu GaoDepartment of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China.
Liang LiDepartment of Bacteriology and Immunology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, 101149, China. cctb@tb123.org.
Yongchang SunDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China. suny@bjmu.edu.cn.

Funding

the Key clinical Projects of Peking University Third Hospital BYSYZD2022014
6 · The paper itself

Abstract

backgroundPatients with pulmonary tuberculosis (TB) achieve microbiological cures following anti-TB treatment; however, post-tuberculosis lung disease (PTLD) persists in some. This study aimed to evaluate whether pre-treatment blood cell count indicators and inflammatory markers could predict the radiologic severity of PTLD.

methodsOverall, 169 patients with pulmonary TB diagnosed at Beijing Chest Hospital were prospectively enrolled. Baseline data were recorded and chest computed tomography (CT) images after the completion of anti-TB treatment were scored from 0 to 24 using a validated system. Patients were classified into mild and severe lung damage groups according to the median value of post-treatment chest CT score. Linear regression and logistic regression analyses were performed to assess the association between baseline inflammatory markers and post-treatment radiologic severity.

resultsThe overall mean age was 39.34 ± 15.37 years; 72.8% were men and 49.7% had a smoking history. The median CT score at the treatment end was 9 (interquartile range: 6–15). Significant positive correlations were observed between post-treatment CT scores and baseline erythrocyte sedimentation rate (ESR) (β = 0.040), high-sensitivity C-reactive protein (hs-CRP) (β = 0.030), white blood cell (WBC) count (β = 0.269), neutrophil percentage (β = 0.121) and count (β = 0.330), monocyte count (β = 4.169), and neutrophil-to-lymphocyte ratio (NLR) (β = 0.272). Negative correlations were identified with hemoglobin (Hb) (β = -0.070) and lymphocyte percentage (β = -0.138). Multivariable logistic regression confirmed the predictors.

conclusionPre-treatment ESR, hs-CRP, WBC count, Hb, neutrophil percentage and count, lymphocyte percentage, monocyte count and NLR are predictive of post-treatment radiologic severity. These findings highlight the potential for using readily available clinical markers to identify high-risk patients and guide personalized management strategies.

Indexed as

BiomarkersTuberculosis, PulmonaryAdultAntitubercular AgentsBlood Cell CountBlood SedimentationC-Reactive ProteinFemaleHumansMaleMiddle AgedProspective StudiesSeverity of Illness IndexTomography, X-Ray ComputedAntitubercular AgentsBiomarkersC-Reactive ProteinBlood cell countsCT scoreInflammatory markersPost-tuberculosis lung diseaseTuberculosis

Identifiers

PMID41126090
PMCPMC12542257

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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.