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A CLINICAL PREDICTION MODEL FOR UNSUCCESSFUL PULMONARY TUBERCULOSIS TREATMENT OUTCOMES
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Abstract
Despite widespread availability of curative therapy, tuberculosis 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 HIV-related severity and isoniazid acetylator status.
Methods: Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly-diagnosed tuberculosis patients in Brazil from 2015-2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary tuberculosis who started first-line anti-tuberculosis therapy and had ≥12 months of follow-up. The endpoint was unsuccessful tuberculosis 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 seven 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: 0.73-0.80) and was well-calibrated (optimism-corrected intercept and slope: -0.12 and 0.89,
Downloaded from https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciab598/6313211 by Fundacao Oswaldo Cruz (FIOCRUZ) user on 14 July 2021
respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model.
Conclusions: The prediction model, using information readily available at treatment initiation, 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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