Quantitative Imaging Analysis to Identify Chronic Respiratory Disease

慢性呼吸系统疾病（CRD），如慢性阻塞性肺病（COPD）和 间质性肺病（ILD）目前是美国第四大死亡原因，但往往仍然 未被诊断和治疗不足直到晚期。目前的研究表明， 与普通人群相比，退伍军人中CRD的患病率和发病率上升。然而，尽管 高患病率和证据支持早期医疗管理改善结局，无筛查 目前存在CRD的程序。胸部计算机断层扫描（CT），一种采用 对于肺癌筛查（LCS），可以检测与CRD相关的肺结构变化，但其 使用受到以下限制：（1）视觉解释的劳动密集型性质和人与人之间的可变性， （2）临床报告，通常仅关注急性发现（肺结节、肺炎）， 慢性病报告不一致。定量成像分析（QIA）技术已经被广泛应用于临床。 它可以直接从胸部客观地检测和量化广泛的病理变化 CT成像数据，通常具有相对于视觉方法增加的灵敏度。我们主张QIA的应用 在组织良好的LCS计划的主持下，临床获得的胸部CT数据代表了 有机会在高风险退伍军人人群中识别和描述未诊断的CRD。 我们建议开发和验证一种临床工具，即基于定量成像分析的风险 总结（QIA-RS），将LCS胸部CT的成像信息转化为实际证据 在三个CRD领域：肺功能损害，症状和功能状态，以及未来的呼吸 保健利用。QIA将在VA防火墙后使用TRM批准的软件进行，以评估 CRD的特征（例如肺气肿、气道壁厚度、间质性肺异常和全肺 容量）对入组VA Boston LCS项目的患者的存档和新获取的胸部CT数据进行分析 （2017-2019年间有4，777例独特转诊，每年约有1400例新转诊）。临床确定的肺量测定 将用于训练和验证模型，以预测肺功能 使用QIA特征作为预测因子的肺功能损害（QIA-RS肺功能损害领域-目标1）。因为 未确诊CRD的个体（我们QIA-RS工具的目标人群） 在文献中描述的特征，我们建议招募以前没有肺部疾病史的个体， 研究访视的LCS时间（n=300），其中肺功能、呼吸道症状和功能 将评估健康状况（运动能力、健康相关生活质量），并用于确定QIA阈值- 评估与损伤相关的特征（目标2 - QIA-RS呼吸症状和功能状态 域）。我们将通过电话和医疗记录审查对目标2中招募的个体（n=300）进行随访， 评估前瞻性（a）呼吸事件（电话，门诊，紧急护理/急诊， 因呼吸道症状住院治疗）和（B）新的呼吸道药物使用，并将整合 关于肺功能和呼吸道症状（目标2）以及常见和低丰度炎症的数据 作为呼吸结局预测因子的QIA评估特征的风险评估指标（目标3 - QIA-RS呼吸保健利用域）。经验证的QIA-RS工具将提供简洁的 与CRD相关的风险报告沿着可操作的护理建议，代表了一个可扩展的， 用于识别退伍军人中先前未诊断CRD并对其进行风险分层的基于成像的解决方案。这 QIA技术在临床确定的成像研究中的应用代表了一种创新和有效的方法， 利用现有数据促进为退伍军人提供个性化护理，并将协助 在组织一级为疾病管理分配资源。

Chronic respiratory diseases (CRDs), such as chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) are currently the 4th leading cause of death in the U.S., yet often remain undiagnosed and under-treated until the advanced stages. Current research suggests an increased prevalence and rising incidence of CRDs among Veterans relative to the general population. Yet, despite a high prevalence and evidence supporting improved outcomes with early medical management, no screening programs currently exist for CRDs. Chest computed tomography (CT), a medical imaging modality employed for lung cancer screening (LCS), can detect structural changes in the lungs associated with CRDs, but their use has been limited by (1) the labor-intensive nature and inter-person variability of visual interpretation of images, (2) clinical reports which are often focused solely on acute findings (lung nodules, pneumonia) with inconsistent reporting of chronic conditions. Quantitative imaging analysis (QIA) techniques have been developed which can objectively detect and quantify a broad range of pathological changes directly from chest CT imaging data, often with increased sensitivity relative to visual methods. We assert the application of QIA to clinically obtained chest CT data within the auspices of well-organized LCS program represents an opportunity to identify and characterize undiagnosed CRDs among a high-risk Veteran population. We propose to develop and validate a clinical tool, the Quantitative Imaging Analysis-based Risk Summary (QIA-RS), which will translate imaging information from LCS chest CTs into practicable evidence in three CRD domains: lung function impairment, symptoms and functional status, and future respiratory healthcare utilization. QIA will be performed using TRM-approved software behind the VA firewall to assess features of CRD (e.g. emphysema, airway wall thickness, interstitial lung abnormalities, and total lung capacity) on archived and newly acquired chest CT data from patients enrolled in the VA Boston LCS program (4,777 unique referrals between 2017-2019, with ~1400 new referrals/year). Clinically-ascertained spirometry available in approximately 2,400 subjects, will be used to train and validate models to predict lung function impairment using QIA features as predictors (QIA-RS lung function impairment domain – Aim 1). Because individuals with undiagnosed CRDs (the target population for our QIA-RS tool) have been incompletely characterized in the literature, we propose to recruit individuals with no previous history of lung disease at the time of LCS (n=300) for an in-person study visit where lung function, respiratory symptoms, and functional status (exercise capacity, health related quality of life) will be assessed and used to identify thresholds of QIA- assessed features associated with impairments (Aim 2 – QIA-RS respiratory symptom and functional status domain). We will follow individuals recruited in Aim 2 (n=300) via telephony and medical record review for 12 months to assess prospective (a) respiratory events (telephone, outpatient, urgent care / emergency, hospitalization encounters for respiratory symptoms) and (b) new respiratory medication use and will integrate data on lung function and respiratory symptoms (Aim 2) and common and low abundance inflammatory markers to refine risk estimates for QIA-assessed features as predictors of respiratory outcomes (Aim 3 – QIA-RS respiratory healthcare utilization domain). The validated QIA-RS tool, which will provide succinct reports of risks associated with CRDs along with actionable recommendations for care, represents a scalable, imaging-based solution to identify and risk stratify previously undiagnosed CRDs among Veterans. This application of QIA technology to clinically-ascertained imaging studies represents an innovative and efficient use of existing data to promote the delivery of personalized care for individual Veterans and will assist in resource allocation for disease management at the organizational level.