Machine-Learning-Assisted Risk Stratification for Tuberculosis Infection in High-Risk Groups Receiving Immunosuppressive Therapy

Background Individuals receiving immunosuppressive therapy, solid-organ transplant recipients, household contacts of patients with tuberculosis, and healthcare workers are at increased risk of Mycobacterium tuberculosis infection and progression to tuberculosis disease. However, currently available immunological tests cannot reliably distinguish tuberculosis infection from active tuberculosis disease or predict individual progression to disease. We aimed to assess the prevalence of immunological evidence of tuberculosis infection across clinically and epidemiologically distinct risk groups and to explore the potential of machine-learning methods for individualised risk stratification. Methods In this observational study, 544 adults aged 18–75 years were assessed between 2023 and 2026. Participants included patients with pulmonary tuberculosis, tuberculosis-facility healthcare workers, healthcare workers in the general medical network, household and occupational contacts of patients with tuberculosis, patients with rheumatic diseases receiving immunosuppressive therapy, patients undergoing assessment for heart transplantation, heart-transplant recipients, and healthy controls. Participants underwent clinical, epidemiological, radiological, and immunological assessment, including interferon-γ release assays (ELISPOT and QuantiFERON-TB Gold) and the tuberculosis recombinant allergen skin test. Individuals with positive immunological tests underwent additional assessment to exclude active tuberculosis. Logistic regression and decision-tree models were developed separately for risk groups using demographic, epidemiological, and immunosuppressive-treatment variables. Results The prevalence of positive immunological tests was highest among heart-transplant recipients (15 [53·6%] of 28), followed by household contacts (12 [41·4%] of 29), patients undergoing pre-transplant assessment (5 [35·7%] of 14), and tuberculosis-facility healthcare workers (21 [22·6%] of 93). Positivity was lower among patients with rheumatic diseases receiving immunosuppressive therapy (11 [13·9%] of 79) and healthcare workers in the general medical network without documented tuberculosis exposure (7 [10·5%] of 67), and was 3 (4·4%) of 69 among healthy controls. Compared with healthy controls, positivity was significantly higher among tuberculosis-facility healthcare workers, household contacts, patients with rheumatic diseases, and pre-transplant and heart-transplant populations. Among heart-transplant recipients, tacrolimus and everolimus had the largest positive coefficients in the logistic regression model (0·412 and 0·284, respectively). Decision-tree analysis identified age (feature importance 0·525), tacrolimus treatment (0·329), and corticosteroid treatment (0·146) as the main classification features. In patients with rheumatic diseases, biological therapy was the most important feature (0·442), followed by age (0·365) and sex (0·193). Conclusion The prevalence of immunological evidence of tuberculosis infection varied substantially between risk groups and was particularly high among household contacts and heart-transplant recipients. The findings suggest that epidemiological exposure and the type of immunosuppressive therapy should be considered jointly when assessing tuberculosis infection risk. Exploratory machine-learning analyses indicate that treatment-related variables might contribute to individualised risk stratification, particularly among transplant re