Coinfections of Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) impose a substantial global health burden. Patients with MTB infection face a heightened risk of progression to incident active TB, which preventive therapy can mitigate. Current testing methods often fail to identify individuals who subsequently develop incident active TB.
We developed random forest models to predict incident active TB using patients' medical data at HIV-1 diagnosis. Training our model involved utilizing clinical data routinely collected at enrollment from the Swiss HIV Cohort Study (SHCS). This dataset encompassed 55 PWH who developed incident active TB six months post-enrollment and 1432 matched PWH without TB enrolled between 2000-2023. External validation utilized data from the Austrian HIV Cohort Study (AHIVCOS), comprising 43 people with incident active TB and 1005 people without TB.
We predicted incident active TB with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.83 (95% CI 0.8-0.86) in the SHCS. After adjusting for ethnicity and the region of origin and re-fitting the model with fewer parameters, we obtained comparable AUC values of 0.72 (SHCS) and 0.67 (AHIVCOS). Our model outperformed the standard of care (tuberculin skin test and interferon-gamma release assay) in identifying high-risk patients, demonstrated by a lower number needed to diagnose (1.96 vs. 4).
Models based on machine learning offer considerable promise for improving care for PWH, requiring n additional data collection and incurring minimal additional costs while enhancing the identification of PWH that could benefit from preventive TB treatment.