Quantitative Imaging Analysis to Identify Chronic Respiratory Disease

慢性呼吸道疾病(CRD)，如慢性阻塞性肺疾病(COPD)和 间质性肺病(ILD)目前是美国第四大死因，但往往仍然存在 直到晚期才得到诊断和治疗。目前的研究表明， 退伍军人中CRD的患病率和发病率相对于一般人群不断上升。然而，尽管 高患病率和证据支持通过早期医疗管理改善结果，不进行筛查 目前存在针对CRD的计划。胸部计算机断层扫描(CT)，一种使用的医学成像方式 对于肺癌筛查(LCS)，可以检测与CRD相关的肺部结构变化，但其 使用受到以下因素的限制：(1)视觉口译的劳动密集性和人与人之间的可变性 影像，(2)通常只关注急性表现(肺结节、肺炎)的临床报告 慢性病的报告不一致。定量成像分析(QIA)技术已经被 开发了一种可以直接从胸部客观检测和量化广泛的病理变化的系统 CT成像数据，通常比目视方法具有更高的灵敏度。我们断言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数据代表了一种 在高危退伍军人人群中识别和描述未诊断CRD的机会。 我们建议开发和验证一个临床工具，基于定量成像分析的风险 摘要(QIA-RS)，这将把来自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 医疗保健利用率。QIA将在退伍军人管理局防火墙后使用TRM批准的软件执行，以评估 慢性阻塞性肺疾病的特征(如肺气肿、气道壁厚度、间质性肺异常和全肺 容量)对登记在VA Boston LCS计划中的患者的存档和新获得的胸部CT数据 (2017-2019年期间有4,777例独特的转诊，每年约有1,400例新转诊)。临床确诊的肺活量测定法 在大约2400名受试者中可用，将用于训练和验证预测肺功能的模型 使用QIA特征作为预测因素的肺功能损害(QIA-RS肺功能损害域-目标1)。因为 患有未诊断CRD的个人(我们的QIA-RS工具的目标人群)一直不完全 在文献中，我们建议招募以前没有肺部疾病病史的人在 LCS(n=300)面对面研究访问的时间，其中包括肺功能、呼吸道症状和功能性 将评估状态(运动能力、与健康相关的生活质量)，并用于确定QIA的阈值- 评估与损伤相关的特征(AIM 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跟踪AIM 2(n=300)招募的12人 评估预期的(A)呼吸道事件(电话、门诊、紧急护理/紧急情况、 住院遇到呼吸道症状)和(B)新的呼吸系统药物使用，并将结合 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 改进QIA评估特征的风险估计作为呼吸结果预测因子的标记(目标3- QIA-RS呼吸保健利用领域)。经过验证的QIA-RS工具，它将提供简洁的 与CRD相关的风险报告以及可操作的护理建议，代表了一种可扩展、 基于成像的解决方案，用于识别退伍军人中以前未诊断的CRD并对其进行风险分层。这 QIA技术在临床确证影像研究中的应用是一种创新和高效的方法 利用现有数据促进为退伍军人个人提供个性化护理，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.