Prediction of tuberculosis-specific mortality for older adult patients with pulmonary tuberculosis

BackgroundThis 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.MethodsWe conducted a retrospective cohort study and randomly selected 528 old...

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Bibliographic Details
Main Authors: Sihua Wang, Ruohua Gu, Pengfei Ren, Yu Chen, Di Wu, Linlin Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Public Health
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Online Access:https://www.frontiersin.org/articles/10.3389/fpubh.2024.1515867/full
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Summary:BackgroundThis 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.MethodsWe 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.ResultsThe 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).ConclusionThe 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.
ISSN:2296-2565