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

项目摘要 人们越来越认识到，结核病(TB)后肺部疾病(PTLD)是常见的，并导致 严重的发病率和死亡率。然而，在一些患者中，肺损伤的变化是不同的。 在完成结核病治疗后，病情有所改善，其他情况恶化。此外，肺活量测定法--标准方法 评估肺功能--这在结核病负担高的国家并不常见。因此，要确定结核病后的优先顺序 对于可能从早期干预中受益的患者，迫切需要更好地促进早期识别 有患PTLD风险的患者的比例。 该应用程序的总体目标是评估有助于患者早期识别的新方法 PTLD的风险最大。中心假设是患者在治疗中(坚持行为)和新的治疗后 治疗因素(咳嗽频率和声学特征)将改善PTLD的危险分层。中环 假设将通过追求三个具体目标来检验：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)评估咳嗽频率和测量的声学特征 一种新的移动应用程序，作为非侵入性、廉价的肺活量测量代理，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美国国立卫生研究院提供初步数据 R01应用程序评估基于APP的咳嗽测量作为监测工具用于罕见但严重的结核病后 结果包括慢性阻塞性肺病、结核病复发和死亡率。 Huddart博士的职业目标是成为一名独立调查员，专注于了解穷人的驱动因素 为避免结核病相关发病率和死亡率的干预措施提供信息。至 支持她走向独立的道路，拟议的工作将与专门的、多学科的 指导团队和以患者为中心的结果评估(目标1)、机器学习(目标2)和培训 动态结果建模(目标3)。加州大学旧金山分校是一个致力于青少年的杰出环境。 拥有广泛的研究和职业发展资源的调查人员。因此，K01奖将提供 Huddart博士拥有重要的指导、培训、资源和经验，将成为 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.