Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings

Abstract

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% CI 8.0-29.2) among child contacts, 4.8% (3.0-7.7) among adult contacts, 5.0% (1.6-14.5) among migrants, and 4.8%(1.5-14.3) among immunocompromised groups. We confirmed highly variable estimates within risk groups, necessitating an individualized approach to risk-stratification. We thus developed a personalised 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 (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 crude current approach to TB risk estimation among people with LTBI, in favour of our evidence-based and patient-centered method, in settings aiming towards pre-elimination worldwide. J.S.D.'s institution receives investigator-initiated research grants and consultancy income from GileadSciences, AbbVie, Bristol Myers Squibb and Merck. The Burnet Institute receives funding from the Victorian Government Operational Infrastructure Fund. C.L. reports honoraria from Chiesi, Gilead, Insmed, Janssen, Lucane, Novartis, Oxoid, Berlin Chemie (for participation at sponsored symposia) and from Oxford Immunotec (to attend a scientific advisory board meeting), all outside the submitted work. M.S. reports receipt of test kits free of charge from Qiagen and from Oxford Immunotec for investigator-initiated research projects. I.A. reports receiving free test kits from Qiagen for an

Evaluating patient education resources for supporting treatment decisions in latent tuberculosis infection

“…1 in 10 people with LTBI will progress to active TB), which may lead patients to form inaccurate representations around their own risk. Newly developed tools such as the Periskope.TB (http://periskope.org/tb-risk-predictor/), which take into consideration individual demographics and medical characteristics, may help to communicate personal risk to patients (Gupta et al, 2020). However, further research is required to understand how personalised risk estimates influence treatment decisions.…”

Anticipating the future: prognostic tools as a complementary strategy to improve care for patients with febrile illnesses in resource-limited settings

“…Depending on the temporal relationship between these baseline data (predictors) and the disease or health state in question (the outcome), these predictions are either diagnostic or prognostic. 8 Often the distinction between diagnosis and prognosis is clear: integration of clinical, laboratory and radiological information to predict whether a patient may have infective endocarditis (Duke criteria) 15 is a diagnostic process, whereas predicting the probability that an individual may develop active tuberculosis (TB) within the next 2 years based on their demographics, medical history and latent TB infection test result (PERISKOPE-TB) 16 is easily recognisable as prognosis (figure 1).…”

Blood transcriptomic biomarkers for tuberculosis screening: time to redefine our target populations?

“…Future approaches could also consider integrating biomarker results into multivariable pre diction tools to improve discrimination between disease states. 10 Second, how individuals with a positive blood transcriptomic biomarker test should be treated remains to be determined. Thus far, clinical trials have dichotomised the management of tuberculosis into preventive treatment for latent infection (with fewer agents for shorter periods of time) and treatment of tuberculosis disease (with more agents for longer periods of time).…”