Evidence map›Paper›PMID 39845975›Full record

ArticleFrontiers in immunology2024

Diagnostic accuracy of

Lifan Zhang, Yuanchun Li, Xiaoqing Zou, Huimin Ma, Mengqiu Gao, Qiping Ge, Yueqiu Zhang, Zhengrong Yang, Xinuo Song, Qiwen Yang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2024. 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. 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

11 authors.

Lifan Zhang *Division of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yuanchun Li *Division of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiaoqing Zou *Division of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Huimin MaDivision of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Mengqiu GaoDepartment of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Qiping GeDepartment of Tuberculosis, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Yueqiu ZhangDivision of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhengrong YangDivision of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xinuo SongDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Qiwen YangDepartment of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Xiaoqing LiuDivision of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to evaluate the diagnostic accuracy of a Method: Febrile patients with suspected active TB (ATB) were consecutively enrolled. The frequencies and proportions of MTB-specific T cells secreting IFN-γ, IL-2, and TNF-α were detected at the single-cell level by triple-color FluoroSpot assay. The diagnostic index was fitted with a binary logistic regression model, and the diagnostic accuracy was evaluated according to the receiver operating characteristic (ROC) curve. The sensitivity, specificity, predictive values (PV), and likelihood ratios (LR) were calculated. Result: A total of 210 febrile patients were enrolled, 53 patients were diagnosed with ATB (28 pathogen-confirmed vs. 25 clinically diagnosed) and 157 patients were non-ATB (84 with latent tuberculosis infection (LTBI) vs. 73 uninfected with MTB). Additionally, 30 pathogen-confirmed ATB patients were assembled. When diagnosing ATB, the area under the ROC curve (AUROC) of the MTB-specific triple-color FluoroSpot assay was significantly better than that of T-SPOT.TB (0.882 vs. 0.811, Conclusion: The MTB-specific triple-color FluoroSpot (IFN-γ/IL-2/TNF-α) might be helpful for the differentiation of TB infection status in febrile patients.

Indexed as

FeverMycobacterium tuberculosisTuberculosisAdultAgedDiagnosis, DifferentialFemaleHumansInterferon-gammaInterleukin-2MaleMiddle AgedROC CurveSensitivity and SpecificityTumor Necrosis Factor-alphaYoung AdultInterferon-gammaInterleukin-2Tumor Necrosis Factor-alphaactive tuberculosisFluoroSpotlatent tuberculosis infectiontriple-colorT-SPOT.TB

Identifiers

PMID39845975
PMCPMC11751065

What OpenQuestion holds

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Registered trials

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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.