ArticleFrontiers in public health2024
Prediction of tuberculosis-specific mortality for older adult patients with pulmonary tuberculosis.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Epidemiological, Spatial and Predictive Analysis of Tuberculosis-Related Mortality in the Department of Risaralda (Colombia), 2020-2024.Tropical medicine & international health : TM & IH · 2026Observational
- Development and validation of a primary care-accessible risk prediction model for severe pulmonary TB.IJTLD open · 2026Article
- The Prognostic Value of the Neutrophil-to-lymphocyte Ratio in Older Patients with Pulmonary Tuberculosis.Internal medicine (Tokyo, Japan) · 2026Article
- Tuberculosis in older adults (≥ 65 years): Global trends, sex differences, and regional variations, 2000-2023.BMC geriatrics · 2026Article
- Identification of risk factors for latent tuberculosis infection in Xinjiang using machine learning.BMC public health · 2025Article
- A nomogram for predicting unfavorable outcomes of antituberculosis treatment among individuals with AIDS combined with pulmonary tuberculosis in China.Frontiers in immunology · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aims to identify risk factors associated with tuberculosis-specific mortality (TSM) in older adult patients with pulmonary tuberculosis (TB) and to develop a competing risk nomogram for TSM prediction. Methods: We conducted a retrospective cohort study and randomly selected 528 older adult pulmonary TB patients hospitalized in designated hospitals in Henan Province between January 2015 and December 2020. The cumulative incidence function (CIF) was calculated for both TSM and non-tuberculosis-specific mortality (non-TSM). A Fine and Gray proportional subdistribution hazards model and a competing risk nomogram were developed to predict TSM in older adult patients. Results: The 5-year cumulative incidence functions (CIFs) for TSM and non-TSM were 9.7 and 9.4%, respectively. The Fine and Gray model identified advanced age, retreatment status, chest X-rays (CXR) cavities, and hypoalbuminemia as independent risk factors for TSM. The competing risk nomogram for TSM showed good calibration and excellent discriminative ability, achieving a concordance index (c-index) of 0.844 (95% confidence interval [CI]: 0.830-0.857). Conclusion: The Fine and Gray model provided an accurate evaluation of risk factors associated with TSM. The competing risk nomogram, developed using the Fine and Gray model, provided accurate and personalized predictions of TSM.
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