ArticleBMC infectious diseases2025
Predicting treatment outcomes in drug-sensitive pulmonary tuberculosis patients in rural eastern China.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThis study aimed to identify risk factors associated with unsuccessful treatment outcomes among newly diagnosed drug-sensitive pulmonary tuberculosis (PTB) patients in rural eastern China and to develop a prediction model for treatment outcomes.
methodsThis study analyzed 838 newly diagnosed drug-sensitive PTB patients in rural eastern China (2021-2023). Treatment outcomes (unsuccessful treatment) were assessed using WHO guidelines. The cohort was randomly divided into a training set (70%) and a validation set (30%) for internal validation. Multivariate logistic regression identified predictors, including age, malnutrition, comorbidities, hemoglobin levels, and sputum smear grades. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the prediction model by quantifying the net benefit across a range of threshold probabilities.
resultsThe prediction model identified six independent predictors of unsuccessful treatment outcomes: diabetes, chronic lung disease, alcohol use, hypoalbuminemia, anemia, and sputum smear grades. The area under the receiver operating characteristic curve (AUC) was 0.754 (95% CI: 0.676-0.833), indicating good discriminative ability. The model demonstrated moderate accuracy across three risk categories. A nomogram was developed to visually represent the model, enabling clinicians to estimate individual patient risk based on these six predictors. Additionally, an online calculator was created to facilitate easy and practical application of the model in clinical settings. Decision curve analysis (DCA) further validated the clinical utility of the model, showing a significant net benefit across a wide range of threshold probabilities (2-54%), supporting its applicability for guiding clinical decision-making.
conclusionsThe prediction model serves as a valuable tool for clinicians to identify high-risk PTB patients and tailor interventions effectively. This approach can enhance treatment strategies and contribute to better TB control in rural eastern China.
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