A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes

Clin Infect Dis. 2022 Mar 23;74(6):973-982. doi: 10.1093/cid/ciab598.


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.

Keywords: HIV coinfection; epidemiologic research; prediction model; prognosis; pulmonary tuberculosis.

Publication types

  • Observational Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antitubercular Agents / therapeutic use
  • HIV Infections* / complications
  • HIV Infections* / drug therapy
  • HIV Infections* / epidemiology
  • Humans
  • Isoniazid / therapeutic use
  • Models, Statistical
  • Prognosis
  • Treatment Outcome
  • Tuberculosis* / drug therapy
  • Tuberculosis, Pulmonary* / diagnosis
  • Tuberculosis, Pulmonary* / drug therapy


  • Antitubercular Agents
  • Isoniazid