An Integrated Data-Driven Model for Clinical Phenotyping of Tuberculosis Disease Severity.

A common approach to describing tuberculosis (TB) disease severity is to use a binary classification such as "advanced" and "minimal or early disease," though this may not fully capture the range of clinical presentations. As individuals transition through stages of disease, we expect to observe increased bacterial burden and inflammation which corresponds to worsening disease severity and increased risk of a negative outcome. We develop a new method, tuberculosis SeveriTy Assessment Tool for Informed Stratification (TB-STATIS), to understand the various disease severity phenotypes that exist at time of clinical presentation. Our method integrates data from multiple sources (i.e. smear microscopy, chest x-ray findings, symptoms, etc.) to identify a set of disease severity classes and obtain a predicted disease class for each individual given their observed data. Our approach is motivated by the statistical framework used in event-based modeling, a type of data-driven disease progression modeling. We show in simulation TB-STATIS can correctly identify the true set of disease classes with various sample sizes, data sources to integrate, and levels of uncertainty in the observed data. We apply TB-STATIS to two data sets, data from an observational TB cohort in South Africa and data from a global phase 3 clinical trial that tested the non-inferiority of two 4-month regimens compared to the standard 6-month regimen for the treatment of TB. We observe disease classes generated from TB-STATIS correlate with culture conversion, a proxy for TB treatment response. We demonstrate our approach to classifying TB disease severity generates clinically meaningful strata.