Quantitative Imaging Analysis to Identify Chronic Respiratory Disease

定量成像分析识别慢性呼吸道疾病

• 批准号: 10249646
• 负责人: Emily S Wan
• 金额: --
• 依托单位: VA BOSTON HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10249646
• 关键词: Acute; Archives; Blood Tests; Boston; Caring; Cause of Death; Chest; Chronic; Chronic Obstructive Airway Disease; Clinical; Computer software; Computers; Data; Development; Diagnosis; Diagnostic radiologic examination; Diffusion; Disease; Disease Management; Dose; Early Diagnosis; Early treatment; Elderly; Emergency Care; Enrollment; Event; Exhibits; Future; General Population; Health; Healthcare; High Prevalence; Hospitalization; Image; Image Analysis; Impairment; Incidence; Individual; Interstitial Lung Diseases; Literature; Lung; Lung diseases; Lung nodule; Malignant neoplasm of lung; Measurement; Medical; Medical Imaging; Medical Records; Methods; Modeling; Nature; Occupational; Occupational Exposure; Outcome; Outpatients; Pathologic; Patients; Persons; Pharmaceutical Preparations; Pneumonia; Population; Positioning Attribute; Prevalence; Protocols documentation; Pulmonary Emphysema; Recommendation; Recording of previous events; Reporting; Research; Resource Allocation; Respiratory Signs and Symptoms; Risk; Risk Estimate; Risk Factors; Scanning; Services; Smoking; Spirometry; Standardization; Structure; Symptoms; System; Target Populations; Techniques; Technology; Telephone; Testing; Thick; Time; Total Lung Capacity; Training; Translating; Translations; Validation; Veterans; Visit; Visual; base; chest computed tomography; cigarette smoking; exercise capacity; firewall; functional disability; functional status; health care service utilization; health related quality of life; high risk; image archival system; imaging modality; imaging platform; imaging study; improved outcome; inflammatory marker; innovation; inter-individual variation; interstitial; lung cancer screening; military veteran; open source; personalized care; programs; prospective; pulmonary function; quantitative imaging; radiologist; reconstruction; recruit; respiratory; risk stratification; screening program; standard of care; tool; urgent care

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.

慢性呼吸道疾病（CRD），例如慢性阻塞性肺病（COPD）和 间质性肺疾病（ILD）目前是美国第四大死亡原因，但通常仍然是 目前的研究表明，直到晚期才得到诊断和治疗不足。 然而，尽管退伍军人中 CRD 的患病率和发病率相对于普通人群有所上升。 高患病率和证据支持通过早期医疗管理改善结果，无需筛查 目前存在用于 CRD 的程序，这是一种采用的医学成像方式。 用于肺癌筛查 (LCS)，可以检测与 CRD 相关的肺部结构变化，但它们 使用受到以下因素的限制：(1) 视觉解释的劳动密集型性质和人与人之间的差异 图像，(2) 临床报告，通常仅关注急性表现（肺结节、肺炎） 慢性病的定量成像分析（QIA）技术的报告不一致。 开发出可以直接从胸部客观检测和量化广泛病理变化的技术 CT 成像数据通常比视觉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 计划的支持下临床获得的胸部 CT 数据代表了 有机会识别和描述高风险退伍军人群体中未确诊的 CRD。 我们建议开发并验证一种临床工具，即基于定量成像分析的风险 摘要 (QIA-RS)，将 LCS 胸部 CT 的影像信息转化为实用证据 三个 CRD 领域：肺功能损伤、症状和功能状态以及未来呼吸 QIA 将使用 VA 防火墙后面经 TRM 批准的软件进行评估。 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）对参加 VA 波士顿 LCS 项目的患者的存档和新采集的胸部 CT 数据进行分析 （2017 年至 2019 年期间有 4,777 次独特转诊，每年约有 1400 次新转诊）。 可用于大约 2,400 名受试者，将用于训练和验证预测肺功能的模型 使用 QIA 特征作为预测因子的损伤（QIA-RS 肺功能损伤领域 - 目标 1）。 患有未确诊 CRD 的个体（我们的 QIA-RS 工具的目标人群）尚未完全被 根据文献中的特征，我们建议招募以前没有肺部疾病史的个体 LCS 时间（n=300）进行现场研究访问，其中肺功能、呼吸道症状和功能 状态（运动能力、健康相关的生活质量）将被评估并用于确定 QIA- 的阈值 评估与损伤相关的特征（目标 2 – QIA-RS 呼吸道症状和功能状态 我们将通过电话和医疗记录审查来跟踪目标 2 中招募的个人 (n=300) 12 名。 评估预期 (a) 呼吸系统事件（电话、门诊、紧急护理/急诊、 (b) 新的呼吸系统药物使用并将整合 有关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 标记物来完善 QIA 评估特征的风险估计，作为呼吸结果的预测因子（目标 3 – QIA-RS 呼吸保健利用领域）经过验证的 QIA-RS 工具，将提供简洁的信息。 与 CRD 相关的风险报告以及可行的护理建议代表了可扩展的、 基于成像的解决方案，用于识别退伍军人中先前未诊断的 CRD 并进行风险分层。 QIA 技术在临床确定的成像研究中的应用代表了一种创新且高效的方法 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 组织层面疾病管理的资源分配。