ArticleFrontiers in cellular and infection microbiology2021
Combination of Blood Routine Examination and T-SPOT.TB Assay for Distinguishing Between Active Tuberculosis and Latent Tuberculosis Infection.
Article in Frontiers in cellular and infection microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of biomarkers for differentiating active tuberculosis from latent tuberculosis: a systematic review and Bayesian network meta-analysis.Frontiers in microbiology · 2024Pooled it
- Natural history of latent brucellosis and diagnostic performance of B-SPOT: a 24-month prospective cohort study based on large-scale screening in northern China.Emerging microbes & infections · 2026Article
- Diagnostic utility of quantitative interferon-gamma release assays in elderly patients with tuberculosis.Microbiology spectrum · 2026Article
- Systemic vasculitis with latent tuberculosis infection and associated factors: a cross-sectional multicenter study.Clinical rheumatology · 2025Article
- Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025Article
- Tuberculosis Diagnosis: Current, Ongoing, and Future Approaches.Diseases (Basel, Switzerland) · 2024Review
- Development and validation of a diagnostic model to differentiate spinal tuberculosis from pyogenic spondylitis by combining multiple machine learning algorithms.Biomolecules & biomedicine · 2024Article
- A clinical indicator-based prognostic model predicting treatment outcomes of pulmonary tuberculosis: a prospective cohort study.BMC infectious diseases · 2023Article
- Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection.BMC infectious diseases · 2022Article
- A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study.Scientific reports · 2022Article
- Clinical application of metagenomic next-generation sequencing in tuberculosis diagnosis.Frontiers in cellular and infection microbiology · 2022Article
- A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis.Frontiers in cellular and infection microbiology · 2022Article
- Activation Phenotype ofFrontiers in immunology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Distinguishing between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. Methods: Between 2013 and 2019, 2,059 (1,097 ATB and 962 LTBI) and another 883 (372 ATB and 511 LTBI) participants were recruited based on positive T-SPOT.TB (T-SPOT) results from Qiaokou (training) and Caidian (validation) cohorts, respectively. Blood routine examination (BRE) was performed simultaneously. Diagnostic model was established according to multivariate logistic regression. Results: Significant differences were observed in all indicators of BRE and T-SPOT assay between ATB and LTBI. Diagnostic model built on BRE showed area under the curve (AUC) of 0.846 and 0.850 for discriminating ATB from LTBI in the training and validation cohorts, respectively. Meanwhile, TB-specific antigens spot-forming cells (SFC) (the larger of early secreted antigenic target 6 and culture filtrate protein 10 SFC in T-SPOT assay) produced lower AUC of 0.775 and 0.800 in the training and validation cohorts, respectively. The diagnostic model based on combination of BRE and T-SPOT showed an AUC of 0.909 for differentiating ATB from LTBI, with 78.03% sensitivity and 90.23% specificity when a cutoff value of 0.587 was used in the training cohort. Application of the model to the validation cohort showed similar performance. The AUC, sensitivity, and specificity were 0.910, 78.23%, and 90.02%, respectively. Furthermore, we also assessed the performance of our model in differentiating ATB from LTBI with lung lesions. Receiver operating characteristic analysis showed that the AUC of established model was 0.885, while a threshold of 0.587 yield a sensitivity of 78.03% and a specificity of 85.69%, respectively. Conclusions: The diagnostic model based on combination of BRE and T-SPOT could provide a reliable differentiation between ATB and LTBI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.