Evidence map›Paper›PMID 36803117›Full record

ArticleBMC infectious diseases2023

A clinical indicator-based prognostic model predicting treatment outcomes of pulmonary tuberculosis: a prospective cohort study.

Mengyao Zhan, Hao Xue, Yuting Wang, Zhuchao Wu, Qin Wen, Xinling Shi, Jianming Wang

Abstract read
In one paragraph

Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Hematochemical hallmarks as markers of pulmonary TB severity: A multicenter cross-sectional study.Journal of clinical tuberculosis and other mycobacterial diseases · 2025
    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

7 authors.

Mengyao Zhan *Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166, Nanjing, China.
Hao Xue *Department of Chronic Communicable Diseases, Yancheng Center for Disease Control and Prevention, 224002, Yancheng, China.
Yuting Wang *Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166, Nanjing, China.
Zhuchao WuDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166, Nanjing, China.
Qin WenDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166, Nanjing, China.
Xinling ShiDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166, Nanjing, China.
Jianming WangDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Ave. Nanjing, 211166, Nanjing, China. jmwang@njmu.edu.cn.

Funding

Medical Research Project of Jiangsu Health Commission ZDB2020013Nanjing Major Science and Technology Project 2021-11005National Natural Science Foundation of China 81973103Yancheng Medical Science and Technology Development Program YK2016021
6 · The paper itself

Abstract

objectivesIdentifying prognostic factors helps optimize the treatment regimen and promote favorable outcomes. We conducted a prospective cohort study on patients with pulmonary tuberculosis to construct a clinical indicator-based model and estimate its performance.

methodsWe performed a two-stage study by recruiting 346 pulmonary tuberculosis patients diagnosed between 2016 and 2018 in Dafeng city as the training cohort and 132 patients diagnosed between 2018 and 2019 in Nanjing city as the external validation population. We generated a risk score based on blood and biochemistry examination indicators by the least absolute shrinkage and selection operator (LASSO) Cox regression. Univariate and multivariate Cox regression models were used to assess the risk score, and the strength of association was expressed as the hazard ratio (HR) and 95% confidence interval (CI). We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). Internal validation was conducted by 10-fold cross-validation.

resultsTen significant indicators (PLT, PCV, LYMPH, MONO%, NEUT, NEUT%, TBTL, ALT, UA, and Cys-C) were selected to generate the risk score. Clinical indicator-based score (HR: 10.018, 95% CI: 4.904-20.468, P < 0.001), symptom-based score (HR: 1.356, 95% CI: 1.079-1.704, P = 0.009), pulmonary cavity (HR: 0.242, 95% CI: 0.087-0.674, P = 0.007), treatment history (HR: 2.810, 95% CI: 1.137-6.948, P = 0.025), and tobacco smoking (HR: 2.499, 95% CI: 1.097-5.691, P = 0.029) were significantly related to the treatment outcomes. The AUC was 0.766 (95% CI: 0.649-0.863) in the training cohort and 0.796 (95% CI: 0.630-0.928) in the validation dataset.

conclusionIn addition to the traditional predictive factors, the clinical indicator-based risk score determined in this study has a good prediction effect on the prognosis of tuberculosis.

Indexed as

Tuberculosis, PulmonaryArea Under CurveHumansPrognosisProspective StudiesTreatment OutcomeBiochemistry examinationClinical indicatorOutcomePrognostic modelTuberculosis

Identifiers

PMID36803117
PMCPMC9940065

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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.