CHICAGO, Feb. 14 (Xinhua) -- New diagnostic tools such as machine learning and precision medicine could help identify tuberculosis (TB) patients with the highest risk of reactivation of the disease, according to a study posted on the website of the University of Michigan (UM) on Thursday.
The researchers used a precision normalization approach to correct for differences in individual basal immune function that revealed a high- and low-reactivation risk.
They showed that identifying multiple biomarkers can provide a more accurate diagnosis for patients. And by introducing multiple biomarker assays in blood tests with powerful analysis tools, the chances for correctly diagnosing TB increase dramatically.
"A multi-array test can provide a more detailed, disease specific glimpse into patient's infection and likely outcome," said study co-author Ryan Bailey, UM professor of chemistry. "Using a precision medicine approach reveals previously obscured diagnostic signatures and reactivation risk potential."
Latent tuberculosis infection (LTBI) affects nearly 2 billion individuals around the world and about 10 percent of those cases result in active tuberculosis.
Currently, LTBI is tested through a skin scratch test or a blood test that can identify one biomarker but cannot distinguish between memory immune response, vaccine-initiated response, and non-tuberculous mycobacteria exposure. The possibility of correctly identifying the disease through these tests is less than 5 percent.
Tuberculosis is treated with an antibiotic regimen, but it also increases the potential side effects of antibiotic resistance.
The study has been published in Integrative Biology.
