A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes.

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.

Lack of Weight Gain During the First 2 Months of Treatment and Human Immunodeficiency Virus Independently Predict Unsuccessful Treatment Outcomes in Tuberculosis.

BACKGROUND: Weight change may inform tuberculosis treatment response, but its predictive power may be confounded by human immunodeficiency virus (HIV). METHODS: We prospectively followed up adults with culture-confirmed, drug-susceptible, pulmonary tuberculosis receiving standard 4-drug therapy (isoniazid, rifampin, pyrazinamide, and ethambutol) in Brazil. We examined median weight change 2 months after treatment initiation by HIV status, using quantile regression, and unsuccessful tuberculosis treatment outcome (treatment failure, tuberculosis recurrence, or death) by HIV and weight change status, using Cox regression. RESULTS: Among 547 participants, 102 (19%) were HIV positive, and 35 (6%) had an unsuccessful outcome. After adjustment for confounders, persons living with HIV (PLWH) gained a median of 1.3 kg (95% confidence interval [CI], -2.8 to .1) less than HIV-negative individuals during the first 2 months of tuberculosis treatment. PLWH were at increased risk of an unsuccessful outcome (adjusted hazard ratio, 4.8; 95% CI, 2.1-10.9). Weight change was independently associated with outcome, with risk of unsuccessful outcome decreasing by 12% (95% CI, .81%-.95%) per 1-kg increase. CONCLUSIONS: PLWH gained less weight during the first 2 months of tuberculosis treatment, and lack of weight gain and HIV independently predicted unsuccessful tuberculosis treatment outcomes. Weight, an easily collected biomarker, may identify patients who would benefit from alternative treatment strategies.