Creating a Veteran's specific risk model to improve lung cancer screening

创建退伍军人的特定风险模型以改善肺癌筛查

• 批准号: 10588292
• 负责人: Eric L Grogan
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588292
• 关键词: Age; Artificial Intelligence; Asbestos; Biometry; Calibration; Carcinogens; Caring; Chronic Obstructive Pulmonary Disease; Clinical Data; Clinical Services; Data; Databases; Department of Defense; Early Diagnosis; Early treatment; Electronic Health Record; Eligibility Determination; Epidemiology; Exposure to; Future; General Population; Goals; Guidelines; HIV; Health; Health Services Research; Hydrocarbons; Individual; Informatics; Ionizing radiation; Link; Lung; Machine Learning; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Modeling; Pattern; Phenotype; Population; Positioning Attribute; Race; Radiology Specialty; Recording of previous events; Reporting; Rest; Rheumatoid Arthritis; Risk; Risk Factors; Services; Signal Transduction; Smoke; Smoker; Smoking; Smoking Behavior; Smoking History; Subgroup; Text; Time; Tobacco use; United States; United States Department of Veterans Affairs; United States Preventative Services Task Force; Veterans; agent orange; burn pit; cancer diagnosis; cancer risk; deep learning; hazard; health data; high risk population; improved; learning strategy; low socioeconomic status; lung cancer screening; military service; model development; mosaic; novel; pulmonary function; screening; screening guidelines; text searching

Current lung cancer screening eligibility guidelines were developed in a civilian population and miss the majority of Veterans who develop lung cancer. The guidelines include 50-80 year old heavy smokers, with a 20 or more pack years history, who either currently smoke or quit within the last 15 years. These criteria only capture 20-35% of lung cancers in the civilian population and Veterans. Furthermore, Veterans suffer from lung cancer at higher rates than the rest of the United States population, smoke more, and have unique exposures to known causes of lung cancer including Agent Orange, asbestos, diesel fumes, ionizing radiation and Open Burn Pit hydrocarbons. Veterans also have additional risk factors for lung cancer such as race, low socio-economic status, previous history of cancer, HIV, rheumatoid arthritis and chronic obstructive pulmonary disease (COPD) each of which have been shown to increase lung cancer risk. Other, population specific models effectively identify at risk subgroups who may benefit from screening, but none of these models have been validated in Veterans and none consider Veterans’ unique risks. A personalized and Veteran-specific model that adds service-related lung cancer risks and leads to the identification of high-risk groups that may benefit from lung cancer screening is needed. The objective of this proposal is to combine general population and Veteran-specific lung cancer risk factors into a Veteran's lung cancer screening eligibility model. Our overall hypothesis is that service histories and novel risk factors can be used in a Veteran-specific lung cancer risk model to broaden the population who may benefit from lung cancer screening. This effort to improve Veterans’ health through the early detection of lung cancer with screening has two aims. In Aim 1 we will define and discover novel phenotypes associated with increased lung cancer risk in Veterans that include longitudinal clinical and military service-specific exposures. We will generate a comprehensive, longitudinal set of lung cancer risk factors from all Veterans who have received care at a VA facility in the last decade. We will use linked Department of Defense service and VA Electronic Health Record (EHR) data to identify service-related exposures and lung cancer risk factors. Using artificial intelligence, we will mine unstructured text data from clinical notes radiological reports to discover novel data pattern (phenotypes) that help predict future lung cancer diagnosis. We hypothesize that we will accurately determine risk variables used in current eligibility models and discover a set of novel Veteran-specific phenotypes associated with lung cancer risk. In Aim 2 we will build a Veteran-specific lung cancer screening model and compare it to existing screening eligibility criteria and models. We will use a combination of standard lung cancer risk variables, military service-specific risk factors and novel discovered EHR lung cancer risk phenotypes to develop a lung cancer screening model. The variables for this model will include a rich mosaic of static and time varying metrics (smoking behavior, lab values, pulmonary function, etc.), lung cancer risk EHR phenotypes (COPD, HIV, etc.), and service-specific risks (Agent Orange, asbestos, etc.). We will compare our new model to the existing lung cancer screening guidelines, the Bach, Liverpool Lung Project and PLCO screening eligibility models. We hypothesize that a Veteran-specific model will identify more at-risk individuals with greater accuracy and calibration compared to current screening eligibility models. With nationally recognized leaders in lung cancer, informatics, VA data use, machine learning, epidemiology, and biostatistics, we are uniquely positioned to accomplish these goals. At the completion of this proposal, a Veteran-specific model will be developed and compared to existing lung cancer screening eligibility models for at-risk Veterans.

目前的肺癌筛查资格指南是在平民群体中制定的，错过了 大多数退伍军人患有肺癌。该指南涵盖 50-80 岁的重度吸烟者，其中 20 或更多年吸烟史，目前吸烟或在过去 15 年内戒烟。这些标准仅 捕获了平民和退伍军人中 20-35% 的肺癌。此外，退伍军人还患有 肺癌发病率高于美国其他人群，吸烟较多，并且具有独特的特征 接触已知的肺癌成因，包括橙剂、石棉、柴油烟雾、电离辐射 和露天燃烧坑碳氢化合物。退伍军人还有其他肺癌危险因素，例如种族、低 社会经济状况、既往癌症史、艾滋病毒、类风湿性关节炎和慢性阻塞性肺病史 疾病（慢性阻塞性肺病）已被证明会增加患肺癌的风险。其他，特定人​​群 模型有效地识别了可能从筛查中受益的高危亚组，但这些模型都没有 已在退伍军人中得到验证，但没有人考虑退伍军人的独特风险。个性化且针对退伍军人的 该模型增加了与服务相关的肺癌风险，并确定了可能会导致肺癌的高危人群 需要从肺癌筛查中获益。该提案的目标是将普通民众结合起来 将退伍军人特定的肺癌风险因素纳入退伍军人肺癌筛查资格模型。我们的 总体假设是，服务历史和新的风险因素可用于退伍军人特异性肺癌 风险模型扩大了可能受益于肺癌筛查的人群。此次努力改进 通过早期发现肺癌和筛查来保证退伍军人的健康有两个目的。 在目标 1 中，我们将定义并发现与肺癌风险增加相关的新表型 退伍军人，包括纵向临床和军事服务特定的暴露。我们将生成一个 来自所有在退伍军人管理局接受护理的退伍军人的全面、纵向的肺癌危险因素 过去十年的设施。我们将使用链接的国防部服务和退伍军人管理局电子健康记录 (EHR) 数据来识别与服务相关的暴露和肺癌风险因素。利用人工智能，我们 将从临床记录放射报告中挖掘非结构化文本数据，以发现新的数据模式 （表型）有助于预测未来的肺癌诊断。我们假设我们将准确地确定 当前资格模型中使用的风险变量，并发现一组新的退伍军人特异性表型 与肺癌风险相关。在目标 2 中，我们将建立一个退伍军人特异性肺癌筛查模型 并将其与现有的筛选资格标准和模型进行比较。我们将使用标准的组合 肺癌风险变量、兵役特定风险因素和新发现的 EHR 肺癌风险 表型来开发肺癌筛查模型。该模型的变量将包括丰富的马赛克 静态和随时间变化的指标（吸烟行为、实验室值、肺功能等）、肺癌风险 EHR 表型（COPD、HIV 等）和特定服务风险（橙剂、石棉等）。我们将 将我们的新模型与现有的肺癌筛查指南、巴赫、利物浦肺项目进行比较 PLCO 筛选资格模型。我们假设退伍军人特定模型将识别更多有风险的人 与当前的筛选资格模型相比，个体具有更高的准确性和校准度。 拥有全国公认的肺癌、信息学、VA 数据使用、机器学习、流行病学领域的领导者， 和生物统计学，我们具有独特的优势来实现这些目标。完成本提案后， 将开发退伍军人特异性模型，并将其与现有的肺癌筛查资格模型进行比较 有风险的退伍军人。