# Novel clinic-based TB diagnostics and testing algorithms for persons with HIV

> **NIH NIH K23** · UNIVERSITY OF WASHINGTON · 2021 · $186,821

## Abstract

PROJECT SUMMARY/ABSTRACT
Tuberculosis (TB) is the leading cause of mortality in people with HIV (PWH) in sub-Saharan Africa, due in part
to delays in diagnosis and timely treatment. While HIV-infected persons often first seek care at peripheral clinics,
TB diagnostic testing equipment located in centralized laboratories require time-consuming specimen transport
and processing, resulting in delayed diagnosis and patient drop-out from care. Use of a point-of-care urine-based
test detecting mycobacterial lipoarabinomannan (LAM) has been successful in reducing mortality in a hospital
setting, but the existing test is insensitive in ambulatory patients. The largest burden of undiagnosed TB is in
PWH in non-hospital settings, and clinic-based testing assays and improved algorithms are urgently needed. A
promising novel, rapid, urine-based tests (nuLAM) has been developed that may overcome many barriers to TB
diagnosis, but has not yet been tested in a clinical setting. My central hypothesis is that rational use of next-
generation POC LAM testing will be an effective intervention for PWH in South Africa. I will test this hypothesis
with the following specific aims: 1) To develop an optimized clinical algorithm to identify HIV-infected outpatients
for TB testing using POC uLAM and guide evaluation of nuLAM. 2) To determine the diagnostic accuracy of the
next-generation LAM tests to detect TB in a population of HIV-infected outpatients attending a rural clinic in
South Africa, compared to rigorous validation standards of TB culture and GeneXpert. 3) To estimate the
population-level impact of nuLAM testing of outpatient PWH through costing and decision analysis. My career
goal is to become a physician-scientist with a focus on developing and implementing diagnostic approaches to
ultimately reduce morbidity and mortality from HIV-associated TB. I will use the opportunity of the K23 award to
obtain mentorship in predictive modeling, implementation science, and diagnostics science. These skills are
highly complementary to my strong background in epidemiologic methods and clinical research, and will enable
me to translate epidemiologic research findings into tools to help clinicians and policymakers improve
approaches to TB diagnosis. At the completion of the research and mentoring outlined, I will be well-positioned
to launch an independent research career with the ultimate goal of improving diagnosis, treatment, and care of
HIV-associated TB in sub-Saharan Africa.

## Key facts

- **NIH application ID:** 10126801
- **Project number:** 5K23AI140918-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** ADRIENNE E SHAPIRO
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $186,821
- **Award type:** 5
- **Project period:** 2019-03-20 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10126801

## Citation

> US National Institutes of Health, RePORTER application 10126801, Novel clinic-based TB diagnostics and testing algorithms for persons with HIV (5K23AI140918-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10126801. Licensed CC0.

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