Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings
Autor: | Claudia C. Dobler, Laura Muñoz, Joseph Doyle, Berit Lange, Gerrit Woltmann, Takashi Yoshiyama, José Domínguez, Steffen Geis, Christoph Lange, David Roth, Dominik Zenner, Pranabashis Haldar, Neus Altet, James C. Johnston, Anja M. Hauri, Rosa Sloot, Alexei Yavlinsky, Maria Krutikov, Frank van Leth, Marc Lipman, Christine Roder, Ibrahim Abubakar, Molebogeng X Rangaka, Thomas Stig Hermansen, Rishi K Gupta, Martina Sester, Claire J. Calderwood, Robert W Aldridge, Jean-Pierre Zellweger, Roland Diel, Matteo Quartagno, Mahdad Noursadeghi, Maximilian C. Aichelburg, Andrew Copas, Giovanni Sotgiu, Kamila Romanowski, Connie Erkens |
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Přispěvatelé: | APH - Global Health, APH - Methodology, Global Health, AII - Infectious diseases, Health Sciences |
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
0301 basic medicine
Adult Male medicine.medical_specialty Risk predictor Tuberculosis Adolescent Tuberculosis/diagnosis Low transmission General Biochemistry Genetics and Molecular Biology Mycobacterium tuberculosis 03 medical and health sciences 0302 clinical medicine Tuberculosis diagnosis Latent Tuberculosis Risk Factors Internal medicine Mycobacterium tuberculosis/pathogenicity medicine Humans Child biology business.industry Tuberculin Test General Medicine Random effects model biology.organism_classification medicine.disease Prognosis 3. Good health 030104 developmental biology 030220 oncology & carcinogenesis Meta-analysis Female Latent Tuberculosis/diagnosis business Cohort study |
Zdroj: | Nature medicine, 26(12), 1941-1949. Nature Publishing Group Nature Medicine r-IGTP. Repositorio Institucional de Producción Científica del Instituto de Investigación Germans Trias i Pujol instname Nature medicine United Kingdom United States Gupta, R K, Calderwood, C J, Yavlinsky, A, Krutikov, M, Quartagno, M, Aichelburg, M C, Altet, N, Diel, R, Dobler, C C, Dominguez, J, Doyle, J S, Erkens, C, Geis, S, Haldar, P, Hauri, A M, Hermansen, T, Johnston, J C, Lange, C, Lange, B, van Leth, F, Muñoz, L, Roder, C, Romanowski, K, Roth, D, Sester, M, Sloot, R, Sotgiu, G, Woltmann, G, Yoshiyama, T, Zellweger, J-P, Zenner, D, Aldridge, R W, Copas, A, Rangaka, M X, Lipman, M, Noursadeghi, M & Abubakar, I 2020, ' Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings ', Nature Medicine, vol. 26, no. 12, pp. 1941-1949 . https://doi.org/10.1038/s41591-020-1076-0 Nature Medicine, 26(12), 1941-1949. Nature Publishing Group |
ISSN: | 1546-170X 1078-8956 |
DOI: | 10.1038/s41591-020-1076-0 |
Popis: | The risk of tuberculosis (TB) is variable among individuals with latentMycobacterium tuberculosisinfection (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. |
Databáze: | OpenAIRE |
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