Lauren S Peetluk1,2, Peter F Rebeiro1,3, Felipe M Ridolfi4, Bruno B Andrade2,5,6,7,8,9, Marcelo Cordeiro-Santos10,11, Afranio Kritski12, Betina Durovni4, Solange Calvacante4,12, Marina C Figueiredo2, David W Haas2,13, Dandan Liu2, Valeria C Rolla4, Timothy R Sterling2. 1. Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. 2. Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. 3. Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. 4. Instituto Nacional de Infectologia Evandro Chagas, Fiocruz, Rio de Janeiro, Brazil. 5. Multinational Organization Network Sponsoring Translational and Epidemiological Research Initiative, Instituto Brasileiro para Investigação da Tuberculose, Fundação José Silveira, Salvador, Bahia, Brazil. 6. Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil. 7. Universidade Salvador, Laureate Universities, Salvador, Bahia, Brazil. 8. Escola Bahiana de Medicina e Saúde Pública, Salvador, Bahia, Brazil. 9. Curso de Medicina, Faculdade de Tecnologia e Ciências, Salvador, Bahia, Brazil. 10. Fundação Medicina Tropical Dr. Heitor Vieira Dourado, Manaus, Brazil. 11. Universidade do Estado do Amazonas, Manaus, Brazil. 12. Universidade Federal do Rio de Janeiro, Faculdade de Medicina, Rio de Janeiro, Brazil. 13. Department of Internal Medicine, Meharry Medical College, Nashville, Tennessee, USA.
Abstract
BACKGROUND: Despite 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. METHODS: Data 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. RESULTS: Of 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. CONCLUSIONS: Using 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.
BACKGROUND: Despite 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. METHODS: Data 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. RESULTS: Of 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. CONCLUSIONS: Using 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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