Drug Concentration Thresholds Predictive of Therapy Failure and Death in Children With Tuberculosis: Bread Crumb Trails in Random Forests

Clin Infect Dis. 2016 Nov 1;63(suppl 3):S63-S74. doi: 10.1093/cid/ciw471.


Background: The role of drug concentrations in clinical outcomes in children with tuberculosis is unclear. Target concentrations for dose optimization are unknown.

Methods: Plasma drug concentrations measured in Indian children with tuberculosis were modeled using compartmental pharmacokinetic analyses. The children were followed until end of therapy to ascertain therapy failure or death. An ensemble of artificial intelligence algorithms, including random forests, was used to identify predictors of clinical outcome from among 30 clinical, laboratory, and pharmacokinetic variables.

Results: Among the 143 children with known outcomes, there was high between-child variability of isoniazid, rifampin, and pyrazinamide concentrations: 110 (77%) completed therapy, 24 (17%) failed therapy, and 9 (6%) died. The main predictors of therapy failure or death were a pyrazinamide peak concentration <38.10 mg/L and rifampin peak concentration <3.01 mg/L. The relative risk of these poor outcomes below these peak concentration thresholds was 3.64 (95% confidence interval [CI], 2.28-5.83). Isoniazid had concentration-dependent antagonism with rifampin and pyrazinamide, with an adjusted odds ratio for therapy failure of 3.00 (95% CI, 2.08-4.33) in antagonism concentration range. In regard to death alone as an outcome, the same drug concentrations, plus z scores (indicators of malnutrition), and age <3 years, were highly ranked predictors. In children <3 years old, isoniazid 0- to 24-hour area under the concentration-time curve <11.95 mg/L × hour and/or rifampin peak <3.10 mg/L were the best predictors of therapy failure, with relative risk of 3.43 (95% CI, .99-11.82).

Conclusions: We have identified new antibiotic target concentrations, which are potential biomarkers associated with treatment failure and death in children with tuberculosis.

Keywords: boosted classification and regression tree analyses; childhood tuberculosis; drug concentration thresholds; pharmacokinetic variability; random forests.

Publication types

  • Multicenter Study

MeSH terms

  • Adolescent
  • Antitubercular Agents / pharmacokinetics*
  • Antitubercular Agents / therapeutic use*
  • Biomarkers
  • Child
  • Child, Preschool
  • Coinfection
  • Drug Monitoring
  • Drug Therapy, Combination
  • Female
  • HIV Infections / drug therapy
  • Humans
  • Infant
  • Machine Learning
  • Male
  • Models, Statistical
  • Prognosis
  • Sensitivity and Specificity
  • Treatment Failure
  • Treatment Outcome
  • Tuberculosis / diagnosis
  • Tuberculosis / drug therapy*
  • Tuberculosis / mortality*


  • Antitubercular Agents
  • Biomarkers