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Grant Details

Grant Number: 1R01CA282793-01 Interpret this number
Primary Investigator: Han, Summer
Organization: Stanford University
Project Title: Integrating Multiple Electronic Health Records Systems to Improve Lung Cancer Outcomes
Fiscal Year: 2023


Abstract

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.



Publications

Risk model-based management for second primary lung cancer among lung cancer survivors through a validated risk prediction model.
Authors: Choi E. , Luo S.J. , Ding V.Y. , Wu J.T. , Kumar A.V. , Wampfler J. , Tammemägi M.C. , Wilkens L.R. , Aredo J.V. , Backhus L.M. , et al. .
Source: Cancer, 2024-03-01; 130(5), p. 770-780.
EPub date: 2023-10-25.
PMID: 37877788
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Software Application Profile: dynamicLM-a tool for performing dynamic risk prediction using a landmark supermodel for survival data under competing risks.
Authors: Fries A.H. , Choi E. , Wu J.T. , Lee J.H. , Ding V.Y. , Huang R.J. , Liang S.Y. , Wakelee H.A. , Wilkens L.R. , Cheng I. , et al. .
Source: International journal of epidemiology, 2023-12-25; 52(6), p. 1984-1989.
PMID: 37670428
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