Fig. 6.

Receiver operating characteristic (ROC) curve for discriminating relapse patients from cured patients at the start of TB treatment in the discovery/training and validation/test cohorts. A predictive model was generated using glmnet and patients who were recruited in South Africa (discovery cohort) in three phases (subcohort I − III). The predictive model was built using the leave-one-out cross-validation (LOOCV) consisting of 68 individual models (a). The predictive model (average of 68 individual predictions) based on combined clinical, microbiological and immunological parameters was then applied to the TBRU cohort (validation cohort) from Uganda and Brazil (b). Relapse patients can be distinguished from cured patients using six markers measured at diagnosis: BMI, TTP, TNF-β, sIL-6R, IL-12p40 and IP-10 with an area under the curve of 0.819 [95% CI 0.679–0.942] for the training set (a) and 0.718 [95% CI 0.509–0.903] for the validation set (b).