# Characterizing the impact of HIV disease severity in prediction models for  tuberculosis treatment outcomes

> **NIH NIH F31** · VANDERBILT UNIVERSITY · 2020 · $26,315

## Abstract

PROJECT ABSTRACT
The global burden of tuberculosis (TB) is staggering. In 2017, 10 million people developed TB and 1.6 million
people died from TB worldwide. Successful treatment of drug-susceptible TB, defined as the combination of
clinical cure or treatment completion, requires at least six months of therapy. Despite widespread availability of
such treatment, the global treatment success rate is sub-optimal, estimated at only 82% in 2016. TB/HIV co-
infection further complicates treatment of both conditions due to drug-drug interactions, high pill burden, and
diminished immune function. However, the degree to which HIV-related disease characteristics, such as CD4
cell count, HIV-1 RNA viral load, and timing of antiretroviral therapy initiation, impact TB treatment outcomes is
unclear. To optimize successful treatment outcomes, healthcare providers need simple and effective tools at
the start of treatment to identify TB patients at the greatest risk of poor outcomes, and whose prognosis could
be improved through tailored care management and treatment monitoring.
To address these knowledge gaps, this proposal aims to develop a prediction model for TB treatment
outcomes with a focus on characterizing HIV severity. I will also incorporate pharmacogenomic data on
acetylation status, informed by known genetic variants of NAT2 that are involved in the metabolism of
isoniazid. Isoniazid is one of two TB drugs taken for the entire 6 months of standard treatment, and its efficacy
is fundamental to TB cure. The plan to quantify the added value of HIV severity and acetylation status in the
prediction model will be useful to understand whether collection of such data improves TB outcome prediction
and is worthwhile to collect in clinical settings.
This study will leverage existing data from the Regional Prospective Observational Research for Tuberculosis
(RePORT)-Brazil project, an observational cohort co-funded by the NIH and Brazilian Ministry of Health that
has enrolled 940 culture-confirmed, pulmonary TB cases in Brazil. The proposed research will address a
fundamental question about what combination of clinical, epidemiologic, and pharmacogenomic factors are
best able to predict TB treatment outcomes. It will support future patient-centered approaches in TB therapy.
Additionally, the innovative training plan will foster my academic development as I further my predoctoral
training in epidemiology at Vanderbilt University. The combination of strong and dynamic mentorship, relevant
TB-specific knowledge, and focused methodologic training in prediction modeling and pharmacogenomic data
analysis will provide a solid foundation to build a successful career as an independent investigator with
expertise in TB epidemiology.

## Key facts

- **NIH application ID:** 10079183
- **Project number:** 1F31AI152614-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Lauren Saag Peetluk
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $26,315
- **Award type:** 1
- **Project period:** 2020-09-01 → 2021-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10079183

## Citation

> US National Institutes of Health, RePORTER application 10079183, Characterizing the impact of HIV disease severity in prediction models for  tuberculosis treatment outcomes (1F31AI152614-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10079183. Licensed CC0.

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