Prediction models for adverse drug reactions during tuberculosis treatment in Brazil.

BACKGROUND: Tuberculosis (TB) treatment-related adverse drug reactions (TB-ADR) can negatively affect adherence and treatment success rates.

METHODS: We developed prediction models for TB-ADR, considering drug-susceptible pulmonary TB participants who initiated standard TB therapy. TB-ADR were determined by the physician attending the participant, assessing causality to TB drugs, the affected organ system, and grade. Potential baseline predictors of TB-ADR included concomitant medication (CM)-use, HIV-status, glycated hemoglobin (HbA1c), age, body mass index (BMI), sex, substance use, and TB drug metabolism variables (NAT2 acetylator profiles). The models were developed through bootstrapped backwards selection. Cox regression was used to evaluate TB-ADR risk.

RESULTS: There were 156 TB-ADR among 102 (11%) of the 945 participants included. Most TB-ADR were hepatic (n = 82; 53%), of moderate severity (grade 2; n = 121; 78%) and occurred in NAT2 slow acetylators (n = 62; 61%). The main prediction model included CM-use, HbA1c, alcohol-use, HIV-seropositivity, BMI, and age, with robust performance (c-statistic = 0.79, 95% confidence interval (CI): 0.74-0.83) and fit (optimism corrected-slope and intercept of -0.09 and 0.94, respectively). An alternative model replacing BMI with NAT2 had similar performance. HIV-seropositivity (hazard ratio (HR) 2.68, 95%CI 1.75-4.09) and CM-use (HR 5.26, 95%CI 2.63-10.52) increased TB-ADR risk.

CONCLUSIONS: The models, with clinical variables and with NAT2, were highly predictive of TB-ADR.