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

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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.