Identifying older adults who benefit from lung cancer screening

项目总结/摘要 肺癌主要是一种衰老性疾病。肺癌是癌症死亡的头号原因， 美国，每年有127，000人死亡。肺癌筛查（LCS）可以预防肺癌死亡 年龄在50-80岁的老年人中，无晚期合并症。筛查必须平衡立即 对未来防止死亡的潜力的危害。在如何解决这个问题上， 在目前符合条件的1450万人中，确定“足够健康”的老年人。 我的长期目标是通过为老年人提供个性化的评估， 根据健康状况，从LCS中获得净收益，并成为预防性健康干预措施的专家， 老年人拟议项目的目的是研究健康与使用 LCS，然后根据健康状况衡量LCS后立即伤害的风险。我会用不同的方式来定义健康 并确定一个最能预测LCS危害的健康指标。这一提议的核心假设是， LCS在健康状况不佳的人群中很常见，这削弱了其益处并增加了伤害的风险 (e.g.,异常肺部扫描后的侵入性手术并发症）。 我将利用退伍军人健康事务部（VHA）的国家数据，该机构率先开发了一种工具， 通过详细的吸烟史评估LCS资格。我将在以下具体案例中检验我的中心假设： 目标。目的1：确定LCS的使用与筛查前健康之间的关联，通过三个指标进行测量 健康指标-（1）预期寿命，（2）通过筛查获得的寿命年数，以及（3）晚期慢性阻塞性肺疾病 肺部疾病目的2：确定LCS提示的诊断程序的并发症发生率 在使用相同的三个健康指标测量的筛查前健康状况中，使用高级定量 方法（即，联合建模）以说明手术选择和并发症风险的相互关联性质。 这个项目在方法上是创新的，因为我将（1）应用一种新颖的，经过验证的算法来排除 诊断成像，以提供LCS使用和危害的无偏估计，（2）应用联合建模来识别 老年人不良筛查结果的风险较高，（3）利用详细的吸烟和健康数据， VHA是其他地方没有的，一直是以前工作的一个持续障碍。这个项目是 在概念上是创新的，因为我将使用与LCS唯一相关的健康指标。 拟议的项目是重要的，因为它将提供一个丰富的了解如何LCS目前使用 通过识别那些最有可能受益于健康的人来推进我们的知识 LCS。这些知识可以指导老年人及其临床医生进行知情的个性化筛查 在相互竞争的健康需求中优先考虑LCS的决策。该项目直接响应国家 老龄化研究所的优先事项是满足患有多种合并症的老年人的需求，并优化其 通过基于其独特的健康状况的个性化决策，

PROJECT SUMMARY/ABSTRACT Lung cancer is primarily a disease of aging. Lung cancer is the number one cause of cancer death in the United States, with 127,000 deaths annually. Lung cancer screening (LCS) can prevent lung cancer death among older adults ages 50-80 without advanced comorbidities. Screening must balance the risk of immediate harms against the potential to prevent death years in the future. There is a fundamental knowledge gap in how to identify older adults who are “healthy enough” for LCS, among the 14.5 million who are currently eligible. My long-term goal is to maximize the benefit of LCS for older adults by providing them individualized estimates of net benefit from LCS based on health status, and become an expert on preventive health interventions for older adults. The objective of the proposed project is to examine the association between health and use of LCS and then measure risks of immediate harms after LCS by health status. I will define health in various ways and identify a metric of health that best predicts harms of LCS. The central hypothesis of this proposal is that LCS is common among those in the poor health, which undermines its benefit and increases the risk of harm (e.g., complications of invasive procedures after abnormal lung scans). I will leverage national data from Veterans Health Affairs (VHA), which has pioneered a tool to systematically assess LCS eligibility via detailed smoking history. I will test my central hypothesis in the following specific aims. Aim 1: Determine the associations between use of LCS and pre-screening health as measured by three health metrics – (1) life expectancy, (2) life years gained by screening, and (3) advanced chronic obstructive pulmonary disease. Aim 2: Determine the rates of complications from diagnostic procedures prompted by LCS across pre-screening health status measured with the same three health metrics, using advanced quantitative methods (i.e., joint modeling) to account for the interrelated nature of procedure choice and complication risk. This project is methodologically innovative because I will (1) apply a novel, validated algorithm to exclude diagnostic imaging to provide unbiased estimates of LCS use and harms, (2) apply joint modeling to identify older adults at higher risk of adverse screening outcomes, and (3) utilize detailed smoking and health data in the VHA which is not available elsewhere and has been a persistent barrier to prior work. This project is conceptually innovative because I will use health metrics uniquely relevant to LCS. The proposed project is significant in that it will provide a rich understanding of how LCS is currently used across categories defined by health and advance our knowledge by identifying those most likely to benefit from LCS. This knowledge could guide older adults and their clinicians towards informed, individualized screening decisions that prioritize LCS among competing health needs. This project directly responds to the National Institute on Aging’s priorities to meet the needs of older adults with multiple comorbidities and to optimize their health by personalizing decision-making based on their unique health status.