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.
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The individual participant data pooled for this analysis are subject to data sharing agreements with the original study authors. The data might be shared with interested parties by the corresponding authors of the original studies, subject to data sharing agreements.
The final prognostic model developed in this study has been made freely available to enable immediate implementation in clinical practice and independent external validation in new data sets (periskope.org). The code underlying the prediction tool is available at github.com/rishi-k-gupta/PERISKOPE-TB.
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This study was funded by the National Institute for Health Research (NIHR) (DRF-2018–11-ST2-004 to R.K.G. and SRF-2011-04-001 and NF-SI-0616-10037 to I.A.), the Wellcome Trust (207511/Z/17/Z to M.N.) and NIHR biomedical research funding to University College London Hospitals. C.L. is funded by the German Center for Infection Research. J.S.D. receives salary support from the National Health and Medical Research Council (Australia). This paper presents independent research supported by the NIHR. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR or the Department of Health and Social Care. The study funders had no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript. The authors would like to thank all of the research teams involved in the primary studies that contributed data for this analysis.
J.S.D.ʼs institution receives investigator-initiated research grants and consultancy income from Gilead Sciences, 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 Oxford Immunotec (to attend a scientific advisory board meeting), all outside of 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 test kits free of charge from Qiagen for an investigator-initiated research project25. C.E. reports receiving test kits free of charge from Qiagen for investigator-initiated research projects outside of the submitted work. The authors declare no other conflicts of interest.
Peer review information Alison Farrell is the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Risk is stratified by binary latent TB test result, provision of preventative treatment, and indication for screening among participants with untreated latent infection (total n = 80,468 participants). Cumulative risk is estimated using flexible parametric survival models with random effects for the intercept by source study, separately fitted to each risk group. Prevalent TB cases (diagnosed within 42 days of recruitment) are included in this sensitivity analysis. Each plot is presented as point estimates (solid line) and 95% confidence intervals (shaded area). PT = preventative treatment.
Pooled incidence rates are shown on log10 scale among participants with: latent TB infection (LTBI) with no preventative therapy (PT); LTBI commencing PT; and without evidence of LTBI. Rates are further stratified by follow-up interval (0–2 years vs. 2–5 years) and indication for screening (total n = 52,576 participants). Pooled incidence rate estimates were derived from random intercept Poisson regression models, without continuity correction for studies with zero events. Numeric results are shown for the subgroups with untreated latent TB infection in the forest plots in Extended Data Fig. 5. Plots show point estimates (filled circles) and 95% confidence intervals (vertical error bars). No pooled estimate could be calculated for child contacts without evidence of LTBI for the 2–5 year interval since there were no incident events.
Extended Data Fig. 5 Forest plots showing incidence rates by source study among participants with untreated LTBI.
Forest plots are stratified by follow-up interval (0–2 years vs. 2–5 years) and indication for screening (total n = 52,576 participants). Pooled incidence rate estimates (shown as diamonds) were derived from random intercept Poisson regression models, without continuity correction for studies with zero events. Incidence rates per study are shown with a continuity correction of 0.5 for studies with zero events. Plots show study-level point estimates (grey squares) and 95% confidence intervals (CIs; horizontal error bars).
Extended Data Fig. 6 Calibration plots from internal-external validation of prediction model, stratified by validation study.
Data from nine primary validation studies are shown, from internal-external cross-validation of the model (developed among n = 31,090 participants; validated among 25,504 in this analysis). X-axis shows predicted risk, in quintiles, with corresponding Kaplan Meier 2-year risk of incident TB on the Y-axis (95% confidence intervals are shown by vertical error bars).
Forest plots showing recalculation of the C-statistics from internal-external cross validation, limiting validation sets to a, participants who did not receive preventative therapy (n = 23,060 participants); b, participants with a positive LTBI test (n = 9,063 participants); and c, binary LTBI test results (using an average quantitative positive or negative LTBI test result as appropriate, based on the medians among the study population; n = 25,504 participants). ‘TB’ column indicates number of incident TB cases within 2 years of study entry and ‘N’ indicates total participants per study included in analysis. Each forest plot shows point estimates (squares) and 95% confidence intervals (error bars). Pooled estimates are shown as diamonds. Panel d, shows decision curve analyses (n = 6,418 participants) when using the prediction model using a binary LTBI test result, compared to the full prediction model, ‘treat all’ and ‘treat none’ strategies across a range of threshold probabilities (x-axis). Net benefit appeared higher for the binary model than either the strategies of treating all patients with evidence of LTBI, or no patients, throughout the range of threshold probabilities. The full model had highest net benefit across most threshold probabilities.
a, Quantitative results for the tuberculin skin test (TST), QuantiFERON Gold-in-tube (QFT-GIT) and T-SPOT.TB are normalised to a percentile scale using a head-to-head population among whom all three tests were performed from 3 studies including recent TB contacts, migrants and immunocompromised participants (n = 8,335; 158 TB cases). We examined the association between normalised test result and risk of incident TB using Cox proportional hazards models with restricted cubic splines. Normalised results for each test appeared to be associated with similar risk of incident TB. b, Kaplan Meier plots from pooled dataset showing cumulative risk of incident TB, stratified by proximity and infectiousness of index cases among contacts (n = 22,231 participants). There was no evidence of separation of risk of additional subgroups of the ‘other’ (non-smear positive household) contacts stratum. PTB = pulmonary TB; EPTB = extra-pulmonary TB. c, Kaplan Meier plots from pooled dataset showing cumulative risk of incident TB among people with positive latent TB tests, stratified by TB incidence in country of birth among migrants from high TB burden countries (n = 1,031 participants). P value represents Log-rank test. d, Kaplan Meier plots from pooled dataset showing cumulative risk of incident TB among people with positive latent TB tests, stratified by country of birth among recent contacts (n = 5,917 participants). P value represents Log-rank test.
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Gupta, R.K., Calderwood, C.J., Yavlinsky, A. et al. Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings. Nat Med 26, 1941–1949 (2020). https://doi.org/10.1038/s41591-020-1076-0
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