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

> **NIH NIH F30** · JOHNS HOPKINS UNIVERSITY · 2021 · $51,036

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10326602
- **Project number:** 1F30AI165167-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Katherine C McNabb
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $51,036
- **Award type:** 1
- **Project period:** 2021-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10326602

## Citation

> US National Institutes of Health, RePORTER application 10326602, Understanding and Predicting Loss to Follow-up from Multi-Drug Resistant Tuberculosis Treatment in the Setting of High-HIV Burden (1F30AI165167-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10326602. Licensed CC0.

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