# Identifying patients at risk of post-tuberculosis lung disease using novel cough and adherence predictors

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $174,420

## Abstract

PROJECT ABSTRACT
There is increasing recognition that post-tuberculosis (TB) lung disease (PTLD) is common and causes
significant morbidity and mortality. However, changes in lung impairment are heterogenous with some patients
improving and others worsening after completion of TB treatment. Moreover, spirometry – the standard method
of assessing lung function – is not routinely available in high TB burden countries. Thus, to prioritize post-TB
patients who may benefit from early interventions, there is an urgent need to better facilitate early identification
of patients at risk for developing PTLD.
The overall objective of this application is to evaluate novel approaches to facilitate early identification of patients
most at risk for PTLD. The central hypothesis is that patient on-treatment (adherence behavior) and novel post-
treatment (cough frequency and acoustic features) factors will improve risk stratification of PTLD. The central
hypothesis will be tested by pursuing three specific aims: 1) characterize the evolution of lung function post-TB
and its impact on health-related quality of life, 2) evaluate cough frequency and acoustic features measured by
a novel mobile app as a non-invasive, inexpensive proxy for spirometry, and 3) evaluate adherence and cough
feature trajectories as novel predictors of PTLD. The results of this work will provide preliminary data for an NIH
R01 application evaluating app-based cough measurement as a monitoring tool for rarer but serious post-TB
outcomes including COPD, TB recurrence and mortality.
Dr. Huddart’s career goal is to become an independent investigator focused on understanding drivers of poor
outcomes among TB patients in order to inform interventions to avert TB-related morbidity and mortality. To
support her path to independence, the proposed work will be paired with a dedicated, multidisciplinary
mentorship team and training in patient-centered outcomes assessment (Aim 1), machine learning (Aim 2), and
dynamic outcome modelling (Aim 3). UCSF is an outstanding environment that is committed to junior
investigators with extensive resources for research and career development. Thus, the K01 award will provide
Dr. Huddart with the critical mentorship, training, resources and experience to become an international leader in
TB outcomes research.

## Key facts

- **NIH application ID:** 10663732
- **Project number:** 1K01HL165039-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Sophie Huddart
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $174,420
- **Award type:** 1
- **Project period:** 2023-07-20 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10663732

## Citation

> US National Institutes of Health, RePORTER application 10663732, Identifying patients at risk of post-tuberculosis lung disease using novel cough and adherence predictors (1K01HL165039-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10663732. Licensed CC0.

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