Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings - Gupta, Rishi K.; Calderwood, Claire J.; Yavlinsky, Alexei; Krutikov, Maria; Quartagno, Matteo; Aichelburg, Maximilian C.; Altet, Neus; Diel, Roland; Dobler, Claudia C.; Dominguez, Jose; Doyle, Joseph S.; Erkens, Connie; et al. (NATURE MEDICINE, 2020)
The risk of tuberculosis (TB) is variable among individuals with latent Mycobacterium tuberculosis infection (LTBI), but validated estimates of personalized risk are lacking. In pooled data from 18 systematically identified cohort studies from 20 countries, including 80,468 individuals tested for LTBI, 5-year cumulative incident TB risk among people with untreated LTBI was 15.6% (95% confidence interval (CI), 8.0–29.2%) among child contacts, 4.8% (95% CI, 3.0–7.7%) among adult contacts, 5.0% (95% CI, 1.6–14.5%) among migrants and 4.8% (95% CI, 1.5–14.3%) among immunocompromised groups. We confirmed highly variable estimates within risk groups, necessitating an individualized approach to risk stratification. Therefore, we developed a personalized risk predictor for incident TB (PERISKOPE-TB) that combines a quantitative measure of T cell sensitization and clinical covariates. Internal–external cross-validation of the model demonstrated a random effects meta-analysis C-statistic of 0.88 (95% CI, 0.82–0.93) for incident TB. In decision curve analysis, the model demonstrated clinical utility for targeting preventative treatment, compared to treating all, or no, people with LTBI. We challenge the current crude approach to TB risk estimation among people with LTBI in favor of our evidence-based and patient-centered method, in settings aiming for pre-elimination worldwide. The risk of developing active tuberculosis (TB) in individuals with latent TB infection is highly variable within and among different risk groups. A personalized risk predictor was developed to better target preventative treatment to individuals at greatest risk, supporting evidence-based clinical decision-making for latent TB.