Personalized screening for lung cancer: the importance of co-existing chronic conditions to clinical practice and policy

摘要/摘要 肺癌是美国和全世界癌症死亡的主要原因，主要是因为大多数 确诊时，患者已是晚期不治之症。然而，肺癌 使用低剂量计算机断层扫描(LDCT)的筛查(LCS)具有革命性的潜力 通过早期发现肺癌的结果。随着LCS传播到现实世界的环境中 和人群，一个关键的悬而未决的问题是，在临床上发现的益处/危害比率 试验将适用于年龄较大、病情较重的人群。LCS考生面临的基本难题是 与肺癌关系最密切的单一风险因素--吸烟史--也与此密切相关 与非肺癌原因(如慢性阻塞性肺疾病)的发病率和死亡率有关 肺气肿)，它限制了预期寿命，并增加了因 诊断或治疗程序。我们提出的研究的首要目标是准确地 描述这一易患高共病负担的亚群的特征，为他们量化 LCS的好处和坏处，使患者能够做出更明智的决定 LCS。我们的研究将通过更充分地利用真实世界的数据来帮助弥合这一知识差距 描述这一“边缘”候选LCS亚群的特征，减少当前的不确定性 面对患者和提供者。更具体地说，我们建议利用电子健康记录 和55-80岁(n~34,039)接受LDCT年度筛查的患者的索赔数据 2016-2022年不同地理位置的真实世界环境。然后我们将使用这些观测数据 在癌症干预仿真网络中使用经过验证的模型来模拟LCS的数据 在现实世界的美国人口中的结果。通过生成以前不可用的真实世界数据 为了在经过验证的模拟模型中使用，该建议直接响应了改善患者- 以LCS候选人为中心的决策，目前筛选的净收益对他们来说是 高度不确定。

Abstract / summary Lung cancer is the leading cause of cancer death in the US and worldwide, largely because most patients have advanced, incurable disease at the time of diagnosis. However, lung cancer screening (LCS) with low-dose computed tomography (LDCT) has the potential to revolutionize lung cancer outcomes through early detection. As LCS is disseminated into real-world settings and populations, a key outstanding question is whether the benefits/harms ratio found in clinical trials will apply to an older and sicker population. The basic conundrum facing LCS candidates is that the single risk factor most strongly linked to lung cancer -- smoking history -- is also strongly linked to morbidity and death from non-lung cancer causes (e.g. chronic obstructive pulmonary disease emphysema), which limit life expectancy and increase risk of complications from diagnostic or therapeutic procedures. The overarching goal of our proposed study is to precisely characterize this vulnerable subpopulation with high comorbidity burden, quantifying for them the benefits and harms of LCS to enable more informed decision-making by patients contemplating LCS. Our study will help close this knowledge gap by leveraging real-world data to more fully characterize this subpopulation of “marginal” LCS candidates, reducing the uncertainty currently facing patients and providers. More specifically, we propose to leverage electronic health records and claims data for patients ages 55-80 (n~34,039) undergoing annual screening with LDCT in geographically diverse real-world settings from 2016-2022. We will then use these observational data with validated models in the Cancer Intervention Simulation Network to simulate LCS outcomes in the real-world US population. By generating previously unavailable real-world data for use in validated simulation models, this proposal responds directly to calls to improve patient- centered decision-making in LCS candidates for whom the net benefits of screening are currently highly uncertain.