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.

当前的肺癌筛查资格指南是在平民中制定的，错过了 大多数患有肺癌的退伍军人。指南包括50-80岁的烟民，有20个 或更多的包装历史，他们目前在过去15年内吸烟或退出。仅这些标准 在平民和退伍军人中捕获20-35％的肺癌。此外，退伍军人遭受 比美国其他人口更高的肺癌，吸烟更多，并且具有独特 暴露于已知的肺癌原因，包括橙色，石棉，柴油烟雾，电离辐射 和开放燃烧坑烃。退伍军人还具有肺癌的其他危险因素，例如种族，低 社会经济状况，先前的癌症史，HIV，类风湿关节炎和慢性阻塞性肺 疾病（COPD）每种疾病已证明会增加肺癌的风险。其他，特定于人口 有效地识别可能从筛查中受益但没有这些模型的模型 在退伍军人中得到了验证，没有人考虑退伍军人的独特风险。一个个性化和经验丰富的 增加与服务相关的肺癌风险的模型，并导致识别高风险群体 需要受益于肺癌筛查。该提议的目的是结合一般人群 资深特定的肺癌危险因素进入了老兵的肺癌筛查资格模型。我们的 总体假设是，服务历史和新的危险因素可用于资深特异性的肺癌 风险模型扩大可能从肺癌筛查中受益的人群。这项改进的努力 通过筛查对肺癌的早期发现，退伍军人的健康有两个目标。 在AIM 1中，我们将定义和发现与肺癌增加有关的新表型 包括纵向临床和特定于兵役的退伍军人。我们将生成一个 所有在VA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和VA电子健康记录 （EHR）数据以识别服务相关的暴露和肺癌危险因素。使用人工智能，我们 将从临床注释放射学报告中挖掘非结构化的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确定 当前可用性模型中使用的风险变量，发现一组新型的资深特定表型 与肺癌风险有关。在AIM 2中，我们将建立一个资深特定的肺癌筛查模型 并将其与现有的筛选资格标准和模型进行比较。我们将结合标准 肺癌风险变量，特定于军事服务的风险因素和新颖发现的EHR肺癌风险 开发肺癌筛查模型的表型。该模型的变量将包括丰富的马赛克 静态和时间变化的指标（吸烟行为，实验室值，肺功能等），肺癌风险 EHR表型（COPD，HIV等）和特定服务的风险（Agent Orange，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，利物浦肺项目和 PLCO筛选资格模型。我们假设一个资深人士特异性模型将确定更多的高风险 与当前的筛选资格模型相比，具有更高准确性和校准的个体。 随着全国认可的肺癌领导者，信息信息，VA数据使用，机器学习，流行病学， 和生物统计学，我们在实现这些目标方面处于独特状态。该提议完成后， 将开发资深特定模型并将其与现有的肺癌筛查资格模型进行比较 高危老兵。