Integrating Multiple Electronic Health Records Systems to Improve Lung Cancer Outcomes

整合多个电子健康记录系统以改善肺癌结果

基本信息

批准号：
10718073
负责人：
Summer S Han
金额：
$ 65.89万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-07-12 至 2028-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10718073
关键词：
Address Artificial Intelligence Asian Behavioral Biometry California Cancer Etiology Cancer Patient Cancer Survivor Cessation of life Clinical Clinical Informatics Clinical Trials Communities Consensus Cost Savings Data Data Sources Databases Diagnosis Electronic Health Record Eligibility Determination Epidemiology Ethnic Origin General Population Goals Guidelines Health Healthcare Healthcare Systems Immunologic Markers Incidence Individual Link Longterm Follow-up Lung Malignant neoplasm of lung Measures Methods Modeling Morbidity - disease rate Oncology Outcome Patients Pattern Population Population Heterogeneity Public Health Race Radiology Specialty Recording of previous events Records Registries Research Research Personnel Risk Risk Factors Smoke Smoker Smoking Smoking Status Statistical Methods Survivors Thoracic Surgical Procedures Time Translational Research United States Preventative Services Task Force Universities cancer diagnosis cancer risk cancer survival computed tomography screening electronic health record system epidemiologic data ethnic diversity high risk improved interest lung cancer screening mortality neoplasm registry neutrophil never smoker never smoking novel population based predictive modeling randomized trial risk prediction screening screening guidelines sex shared database smoking cessation standard of care targeted treatment tool translational impact

项目摘要

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.

筛查和治疗的最新进展增加了肺癌的数量（LC）表面（〜571,340 LC生存截至2019年）。但是，研究表明，这些生存具有高风险第二个原发性肺癌（SPLC），中位10年的SPLC风险为8.36％初始诊断。此外，SPLC的生存具有更高的死亡率，而与之相比单主要LC。存在许多未解决的挑战：（1）虽然先前的研究确定了几个风险因素 SPLC，这些主要在初次诊断时测量和固定，发现重点是生存曾经抽烟的人。但是，SPLC风险可能受到各种因素的动态变化的影响（例如，戒烟），我们的初步数据表明，SPLC风险在幸存者中仍然很高谁从未抽烟。（2）然而，当前主要用于SPLC的流行病学数据不提供详细数据在初始诊断后测量（3）也没有评估危险因素或预测，从未吸烟幸存者。（4）此外，存在有限的试验证据，以解决是否以及如何以及如何解决的重要临床问题在LC存活中IPLC诊断后继续进行CT筛查，这需要长期随访在临床试验中通常不可行。（5）重要的是，通常缺少有关LC幸存的详细筛选数据在大多数基于人群的数据中。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，以产生证据以告知临床决策。我们的长期目标是通过使用大型EHR数据与新颖的统计数据相结合来改善LC结果在初始诊断后测量的集成患者数据的方法。我们的具体目的是：（目标1）新颖的3向链接以使用来自EHR的EHR建立LC（即Oncoshare-Lung）的集成共享数据库基于社区和学术的医疗保健系统（具有较高比例的种族多样性与加利福尼亚癌症登记处（CCR）相关的亚洲从不吸烟者的; （目标2）提供一组临床通过开发一个预测新颖的统计框架，用于有效管理LC生存的决策工具通过捕获IPLC诊断后测量的数据，动态SPLC风险；（目标3）评估可行性和一种新的因果推理方法的实用性，以评估LC存活中SPLC的有效筛查策略使用EHR。我们将采用一种新的因果推理方法，该方法明确模拟了目标试验（假设随机试验回答感兴趣的问题），以估算长期继续进行CT筛选的影响在不同的资格标准下，术语LC存活。我们预计这项研究的完成将填补通过提供：（1）评估个人动态SPLC风险以识别的临床决策工具：用于量身定制监视的高风险表面，（2）新的分析管道，以评估有效的筛查标准 SPLC和（3）曲面良好的数据库，用于用于LC结果和监视的高影响转化研究在一个种族多样化的人群中，它提供了一个独特的机会来研究SPLC中的关键问题。