Observational studyClinical infectious diseases : an official publication of the Infectious Diseases Society of America2022
A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes.
Observational study in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Predicting unfavorable tuberculosis outcomes using machine learning: a prospective cohort.Tropical medicine and health · 2026Article
- Association of complete blood count parameters with adverse outcomes in acute pediatric pneumonia.Pediatric research · 2026Article
- Clinical epidemiology and risk prediction of unsuccessful treatment outcomes in pulmonary tuberculosis with diabetes mellitus.BMC medicine · 2026Article
- A Novel Clinical Nomogram for Predicting Unfavorable Tuberculosis Treatment Outcomes: A Logistic Regression Risk Model.Journal of epidemiology and global health · 2026Article
- Predicting treatment outcomes in drug-sensitive pulmonary tuberculosis patients in rural eastern China.BMC infectious diseases · 2025Article
- Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models.Journal of medical Internet research · 2025Article
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Effects of missed anti-tuberculosis therapy doses on treatment outcome: a multi-center cohort study.Lancet regional health. Americas · 2025Article
- Trends, prevalence, and determinants of unfavorable tuberculosis treatment outcomes among adult patients in Northeast Ethiopia: The race to achieve a 90 % treatment success rate by 2025.Global epidemiology · 2025Article
- Anti-tuberculosis drug-induced hepatotoxicity and associated risk factors among patients with pulmonary tuberculosis at a tertiary care hospital in Thailand.IJID regions · 2025Article
- Pharmacogenomic associations with HIV-1 virologic suppression in TB/HIV patients.Research square · 2024Article
- Artificial intelligence in tuberculosis: a new ally in disease control.Breathe (Sheffield, England) · 2024Article
- Factors affecting the readiness for hospital discharge of initially treated pulmonary tuberculosis patients in China: a phenomenological study.BMC public health · 2024Article
- Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment.BMC public health · 2024Article
- Prediction Models for Adverse Drug Reactions During Tuberculosis Treatment in Brazil.The Journal of infectious diseases · 2024Article
- Machine learning algorithms using national registry data to predict loss to follow- up during tuberculosis treatment.Research square · 2023Article
- A clinical indicator-based prognostic model predicting treatment outcomes of pulmonary tuberculosis: a prospective cohort study.BMC infectious diseases · 2023Article
- Tuberculosis Treatment Outcomes in Brazil: Different Predictors for Each Type of Unsuccessful Outcome.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Observational
- A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study.Frontiers in cellular and infection microbiology · 2023Article
- Integrating landmark modeling framework and machine learning algorithms for dynamic prediction of tuberculosis treatment outcomes.Journal of the American Medical Informatics Association : JAMIA · 2022Article
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Authors and funding
14 authors.
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Abstract
backgroundDespite widespread availability of curative therapy, tuberculosis (TB) treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status.
methodsData originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures.
resultsOf 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 baseline predictors: hemoglobin, HIV infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic = 0.77; 95% confidence interval, .73-.80) and was well calibrated (optimism-corrected intercept and slope, -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model.
conclusionsUsing information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.
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