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

项目摘要 越来越多的人认识到结核病（TB）后肺部疾病（PTLD）是常见的， 严重的发病率和死亡率。然而，某些患者的肺损伤变化是异质性的 结核病治疗结束后，有些患者病情好转，有些患者病情恶化。此外，肺活量测定法-标准方法 评估肺功能的方法--在结核病高负担国家并不是常规方法。因此，为了优先考虑结核病后 对于可能受益于早期干预的患者，迫切需要更好地促进早期识别 有发生PTLD风险的患者。 本申请的总体目标是评估新方法，以促进早期识别患者 PTLD的风险最大。中心假设是，患者在治疗（依从行为）和新的治疗后， 治疗（咳嗽频率和声学特征）因素将改善PTLD的风险分层。中央 将通过追求三个具体目标来检验假设：1）表征结核病后肺功能的演变 及其对健康相关生活质量的影响，2）评价咳嗽频率和声学特征， 一种新型的移动的应用程序，作为肺功能测定的非侵入性、廉价的替代品，以及3）评估依从性和咳嗽 特征轨迹作为PTLD的新预测因子。这项工作的结果将为NIH提供初步数据。 R 01应用程序评估基于应用程序的咳嗽测量作为罕见但严重的结核病后监测工具 结果包括COPD、TB复发和死亡率。 博士Huddart的职业目标是成为一名独立调查员，专注于了解穷人的司机。 在结核病患者中开展一项关于结核病治疗结果的研究，以便为采取干预措施避免结核病相关发病率和死亡率提供信息。到 支持她的独立之路，拟议的工作将与一个专门的，多学科的 指导团队和培训以患者为中心的结果评估（目标1），机器学习（目标2）， 动态结果建模（目标3）。加州大学旧金山分校是一个杰出的环境，致力于初级 研究人员拥有广泛的研究和职业发展资源。因此，K 01奖将提供 博士Huddart凭借重要的指导、培训、资源和经验，成为国际领先的 结核病成果研究。

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.