Understanding and Predicting Loss to Follow-up from Multi-Drug Resistant Tuberculosis Treatment in the Setting of High-HIV Burden

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Project Summary Globally, Tuberculosis (TB) is one of the leading infectious causes of death and a particular concern in countries with a high HIV burden. With only 57% of cases being successfully treated, multi-drug resistant-TB (MDR-TB) has become a substantial barrier to TB control. High rates of loss to follow up (LTFU) (i.e., missing two or more consecutive months of treatment) are a major contributor to the low MDR-TB treatment success rates. LTFU may lead to additional antibiotic resistance, MDR-TB treatment failure, and death. The World Health Organization recommends that patients at-risk for LTFU be given priority attention, but there is currently no evidence-based way to identify these patients. In order to address this gap, the proposed study will develop a prediction model for LTFU from MDR-TB treatment based on characteristics present at treatment initiation. If accurate, this model will identify the patients who are at high-risk for LTFU and who will draw the greatest benefit from interventions that promote care engagement and retention. Although the reasons for LTFU are complex, past research has yielded a number of potential predictors that will inform the proposed prediction model, including male sex, age, housing instability, alcohol use, substance use, employment status, education level, rural residence, and prior episode(s) of TB. In addition to factors present at treatment initiation, the relationship between LTFU and factors that change throughout treatment, including adverse treatment events and treatment regimen, will be examined to develop a broader understanding of MDR-TB care engagement. The proposed study will be nested within the control arm of a cluster-randomized trial of MDR-TB patients in South Africa (R01 AI104488). The specific aims of the proposed study, titled “Understanding and Predicting Loss to Follow-up from MDR-TB Treatment in the Setting of High-HIV Burden”, are to conduct a nested, retrospective cohort study among patients who were LTFU or successfully completed MDR-TB treatment (i.e., cured or completed treatment) to: (1a) develop a prediction model for LTFU from MDR-TB care based on the patient characteristics available at treatment initiation utilizing LASSO regression and k-fold cross-validation; (1b) adapt the prediction model developed in Aim 1a into a tool that can be used by providers at the point of care to estimate a patient’s risk for LTFU; (1c) determine if type of treatment regimen is a risk factor for LTFU and if it improves the fit of the prediction model developed in Aim 1a; and (2) examine the relationship between LTFU and the timing and burden of adverse treatment effects. This study will be the first to take a predictive modeling approach to guide MDR-TB providers in identifying patients at high-risk for LTFU and prioritizing their receipt of support services in order to ultimately improve MDR-TB treatment outcomes in resource-limited settings. Through the proposed study and training plan, the applicant will gain experience analyzing large, complex longitudinal data and applying machine learning to optimize patient engagement and clinical care.