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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)\u2013related severity and isoniazid acetylator status.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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 \u226512 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\u2013based measures.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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\u2005=\u20050.77; 95% confidence interval, .73\u2013.80) and was well calibrated (optimism-corrected intercept and slope, \u20130.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/cid\/ciab598","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T03:22:02Z","timestamp":1625023322000},"page":"973-982","source":"Crossref","is-referenced-by-count":28,"title":["A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes"],"prefix":"10.1093","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5302-2033","authenticated-orcid":false,"given":"Lauren S","family":"Peetluk","sequence":"first","affiliation":[{"name":"Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA"},{"name":"Division of Infectious Diseases, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, 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