Details

Autor(en) / Beteiligte

• Peetluk, Lauren S
• Rebeiro, Peter F
• Ridolfi, Felipe M
• Andrade, Bruno B
• Cordeiro-Santos, Marcelo
• Kritski, Afranio
• Durovni, Betina
• Calvacante, Solange
• Figueiredo, Marina C
• Haas, David W
• Liu, Dandan
• Rolla, Valeria C
• Sterling, Timothy R

Titel

A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes

Ist Teil von

• Clinical infectious diseases, 2022-03, Vol.74 (6), p.973-982

Ort / Verlag

US: Oxford University Press

Erscheinungsjahr

2022

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• 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. We detail the development and internal validation of a prognostic model, including 7 easily collected variables that accurately predict unsuccessful pulmonary tuberculosis treatment outcome. The model can be applied at the point of care with a nomogram or web application.