ArticleScientific reports2020
Nomogram for individualized prediction of incident multidrug-resistant tuberculosis after completing pulmonary tuberculosis treatment.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Identification of risk factors for latent tuberculosis infection in Xinjiang using machine learning.BMC public health · 2025Article
- Accuracy of Machine Learning in Identifying Drug Resistance in Tuberculosis: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- A nomogram for predicting unfavorable outcomes of antituberculosis treatment among individuals with AIDS combined with pulmonary tuberculosis in China.Frontiers in immunology · 2025Article
- Prognostication of treatment non-compliance among patients with multidrug-resistant tuberculosis in the course of their follow-up: a logistic regression-based machine learning algorithm.Frontiers in digital health · 2023Article
- Integrating serum microRNAs and electronic health records improved the diagnosis of tuberculosis.Journal of clinical laboratory analysis · 2021Article
- Incidence Density and Predictors of Multidrug-Resistant Tuberculosis Among Individuals With Previous Tuberculosis History: A 15-Year Retrospective Cohort Study.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purposes of this study were to construct a comprehensive nomogram for providing a simple, precise and personalized prediction of incident multidrug-resistant tuberculosis (MDR-TB) after completing pulmonary tuberculosis treatment (CPTBT). A matched case-control study (1:2 ratios) was performed between 2005 and 2018. A multivariable Cox regression analysis was used to evaluate independent predictors of incident MDR-TB after the CPTBT. A comprehensive nomogram was developed based on the multivariable Cox model. Overall, 1, 836 participants were included in this study. We developed and validated a simple-to-use nomogram that predicted the individualized risk of incident MDR-TB by using 10 parameters after the CPTBT. The concordance index of this nomogram was 0.833 [95% confidence interval (CI) 0.807-0.859] and 0.871 (95% CI 0.773-0.969) for the training and validation sets, respectively, which indicated adequate discriminatory power. The calibration curves for the risk of incident MDR-TB showed an optimal agreement between nomogram prediction and actual observation in the training and validation sets, respectively. The high sensitivity and specificity of nomogram was indicated by using a receiver operating characteristic curve analysis. Through this clinic tool, TB control executives could more precisely monitor, estimate and intervene the risk of incident MDR-TB among individuals with CPTBT.
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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.