Integrating Multiple Electronic Health Records Systems to Improve Lung Cancer Outcomes

最近在筛查和治疗方面的进展增加了肺癌(LC)幸存者的数量(~571,340人 截至2019年的LC幸存者)。然而，研究表明，这些幸存者患癌症的风险很高。 第二原发肺癌(SPLC)，生存5年后10年SPLC风险中值为8.36% 最初的诊断是。此外，与那些仍然患有SPLC的患者相比，患有SPLC的幸存者的死亡率显著更高。 单主LC。存在许多未解决的挑战：(1)尽管先前的研究确定了以下几个风险因素 SPLC，这些大多是在最初诊断时测量和固定的，结果集中在幸存者身上 曾经吸烟过的人。然而，SPLC的风险很可能受到各种因素动态变化的影响 (例如，戒烟)，我们的初步数据显示，在幸存者中，SPLC的风险仍然很高 从不吸烟的人。(2)然而，目前主要用于SPLC的流行病学数据没有提供详细的数据 在初步诊断后测量，(3)也没有评估从不吸烟的危险因素或预测 幸存者。(4)此外，存在有限的试验证据来解决重要的临床问题，即是否以及如何解决 要在IPLC诊断后继续对LC幸存者进行CT筛查，这需要长期的随访，即 在临床试验中往往是不可行的。(5)重要的是，关于LC幸存者的详细筛查数据通常是缺乏的 在大多数基于人口的数据中。我们计划通过利用电子健康来应对这些多重挑战 记录(EHR)和新的分析方法，以生成证据，为临床决策提供信息。我们的长期合作 目标是通过利用大量的EHR数据和新的统计相结合的SPLC来改善LC结果 整合初步诊断后测量的患者数据的方法。我们的具体目标是：(目标1)使用 使用电子病历建立LC(即OncoShare-LUNG)综合共享数据库的新型三向链接 以社区和学术为基础的医疗保健系统(人口种族多元化，比例较高 亚洲从不吸烟者)链接到加州癌症登记中心(CCR)；(AIM 2)提供一套临床 通过开发一种新的预测统计框架来有效管理LC幸存者的决策工具 通过捕获IPLC诊断后测量的数据来动态SPLC风险；以及(AIM 3)评估可行性 一种新的因果推理方法在评估LC幸存者SPLC有效筛查策略中的应用 使用EHR。我们将应用一种新的因果推理方法，该方法明确模拟目标试验(假设 随机试验，以回答感兴趣的问题)，以评估长期和长期的CT筛查的效果 在不同的资格标准下的长期LC幸存者。我们预计这项研究的完成将填补 通过提供：(1)临床决策工具来评估个体的动态SPLC风险，以确定 用于定制监测的高危幸存者，(2)新的分析管道，以评估有效的筛查标准 SPLC，以及(3)一个精心策划的数据库，用于LC结果和监测的高影响转化研究 在一个种族多元化的人口中，这为审查SPLC的关键问题提供了一个独特的机会。

Recent advances in screening and treatment have increased the number of lung cancer (LC) survivors (~571,340 LC survivors as of 2019). However, studies have shown that these survivors have a high risk for developing second primary lung cancer (SPLC), with the median 10-year SPLC risk of 8.36% after surviving 5 years from the initial diagnosis. Further, survivors with SPLC have significantly higher mortality vs. those who remain with single primary LC. Many unaddressed challenges exist: (1) While prior studies identified several risk factors for SPLC, these are mostly measured and fixed at the time of initial diagnosis, with findings focused on survivors who have ever smoked. However, SPLC risk is likely to be influenced by dynamic changes of various factors (e.g., smoking cessation), and our preliminary data show that SPLC risk remains just as high among survivors who never smoked. (2) Nevertheless, current epidemiologic data mainly used for SPLC do not offer detailed data measured after initial diagnosis, (3) nor have risk factors or predictions been evaluated for never-smoking survivors. (4) Further, limited trial evidence exists to address the important clinical question of whether and how to continue CT screening after IPLC diagnosis among LC survivors, which requires a long-term follow-up that is often not feasible in clinical trials. (5) Importantly, data on detailed screening for LC survivors are typically lacking in most population-based data. We plan to address these multiple challenges by leveraging electronic health records (EHRs) and novel analytical approaches to generate evidence to inform clinical decisions. Our long-term goal is to improve LC outcomes by focusing on SPLC utilizing large EHR data combined with novel statistical methods that integrate patient data measured after initial diagnosis. Our Specific Aims are: (AIM 1) to use a novel 3-way linkage to establish an integrated shared database for LC (i.e., Oncoshare-Lung) using EHRs from community-based and academic healthcare systems (with an ethnically diverse population with a high proportion of Asian never smokers) linked to the California Cancer Registry (CCR) ; (AIM 2) to provide a set of clinical decision tools for efficiently managing LC survivors by developing a novel statistical framework for predicting dynamic SPLC risks by capturing data measured after IPLC diagnosis; and (AIM 3) to evaluate the feasibility and utility of a novel causal inference method to assess efficient screening strategies for SPLC in LC survivors using EHRs. We will apply a new causal inference method that explicitly emulates the target trials (hypothetical randomized trials to answer the question of interest) in estimating the effects of continuing CT screening in long- term LC survivors under varying eligibility criteria. We expect that the completion of this research will fill the critical gaps in SPLC by providing: (1) clinical decision tools to assess individuals’ dynamic SPLC risks to identify high-risk survivors for tailored surveillance, (2) new analytic pipelines to evaluate efficient screening criteria for SPLC, and (3) a well-curated database for high-impact translational research for LC outcomes and surveillance in an ethnically diverse population that provides a unique opportunity to examine critical questions in SPLC.