PAtient-centered mUltidiSciplinary care for vEterans undergoing surgery (PAUSE): a hybrid 1 clinical effectiveness-implementation intervention trial

该项目的长期目标是通过发展和改善退伍军人的健康和长寿 实施循证筛查策略预防第二原发肺死亡 肺癌幸存者的癌症(SPLC)。这一点很重要，因为SPLC与 复发，是幸存者的主要死亡风险之一。尽管它很重要，但还有 对于基本问题，如哪些患者应筛查SPLC，没有循证护理标准 以及能持续多久。随着治疗和筛查的改善以及肺癌幸存者的数量 增加，迫切需要对干预措施进行研究，以实施循证 癌症幸存者的癌症筛查。为解决这方面的需要，我们建议采纳一项证据- 基于风险的策略越来越多地应用于肺癌(IPLC)的初始筛查 分层。风险分层导致了IPLC筛查的显著改进，增加了 在提高筛查效率的同时，还遏制了不断膨胀的医疗成本和缩小差距。 为了实现SPLC的类似改进，我们团队开发并验证了一种预测性 根据SPLC风险对肺癌幸存者进行分层的模型，现已在#年临床环境中试行 这个项目。我们的项目是创新的，因为它是(据我们所知)第一个实施风险的项目 SPLC的分层。我们建议的目标是开发一种针对退伍军人的 根据SPLC风险对退伍军人进行临床分层的差异敏感型临床决策支持工具。我们的 中心假设是：1)我们的模型可以准确地识别SPLC的高危退伍军人 2)在VA临床环境中识别高危患者是可行的。要完成以下任务 研究目标，将追求以下具体目标。目标1将验证和调整我们的 先前开发的退伍军人SPLC风险预测模型，利用患者人口统计数据， 吸烟史、IPLC治疗、来自VA EMR的分期和组织学。目标2将 确定影响SPLC风险分层工具的可行性和可接受性的因素，使用 关注提供商决策和结构性或系统性的混合方法方法 在设施层面上的影响。目标3将把SPLC风险预测模型转化为临床 决策支持工具，并在单个VA站点试用该工具，使用迭代周期来改进工具 领悟。在成功完成这项建议后，预期的研究成果如下 拥有1)最新的退伍军人SPLC风险预测模型和2)可行的临床 基于模型的决策支持工具，临床医生可以使用该工具来识别高危患者 SPLC的风险。此外，本建议书中描述的指导性培训计划将 加快吴朱莉博士发展成为独立的卫生服务研究员。她 导师团队包括Arya博士、Backhus博士和han博士-实施科学领域的领导者， 胸部肿瘤学和预测性建模。从这一建议来看，预期的培训结果 将包括1)高级建模和数据库分析培训以及2)定性培训 方法和实施科学，这将有助于国际和平研究所实现她的研究生涯目标 弥合证据和实践之间的执行差距。此外，通过完成这一点， 项目，吴博士将为CDA-2建立培训基础和初步数据 在高危老年肺癌幸存者中识别当前筛查障碍的应用 并试行旨在克服这些障碍的干预措施。这个项目将产生影响，因为 吴博士是退伍军人管理局国家肿瘤学计划的一部分，因此他的独特地位是指导 未来在全国退伍军人管理局网络中扩大SPLC筛查计划。

The long-term goal of this project is to improve Veteran health and longevity by developing and implementing an evidence-based screening strategy to prevent death from second primary lung cancer (SPLC) in lung cancer survivors. This is important because SPLC is distinct from recurrence and is one of the main mortality risks for survivors. Despite its importance, there is no evidence-based standard-of-care for basic questions like which patients to screen for SPLC and for how long. As treatment and screening improve and the number of lung cancer survivors increase, there is a critical need for research into interventions to implement evidence-based cancer screening for cancer survivors. To address this need, we propose to adopt an evidence- based strategy increasingly used in initial primary lung cancer (IPLC) screening, risk stratification. Risk stratification has led to marked improvements in IPLC screening, increasing screening efficiency while also containing ballooning healthcare costs and reducing disparities. To achieve similar improvements for SPLC, our group has developed and validated a predictive model to stratify lung cancer survivors by risk of SPLC, now to be piloted in a clinical setting in this project. Our project is innovative as it is the first (to our knowledge) to implement risk stratification for SPLC. The objective of our proposal is to develop a Veteran-specific and disparity-sensitive clinical decision support tool to stratify Veterans by SPLC risk in clinic. Our central hypotheses are that 1) our model can accurately identify Veterans at high risk for SPLC and 2) identification of high-risk patients is feasible in a VA clinical setting. To accomplish the study objective, the following specific aims will be pursued. Aim 1 will validate and adapt our previously developed SPLC risk prediction model for Veterans, utilizing patient demographics, smoking history, and IPLC treatment, stage and histology derived from the VA EMR. Aim 2 will identify factors that influence feasibility and acceptability of a SPLC risk stratification tool, using a mixed methods approach to focus on provider decision-making and structural or systemic influences at the facility level. Aim 3 will translate the SPLC risk prediction model into a clinical decision support tool and pilot the tool at a single VA site, using iterative cycles to improve tool uptake. Following successful completion of this proposal, the expected research outcomes are to have 1) an updated optimal SPLC risk prediction model for Veterans and 2) a feasible clinical decision support tool based on the model, which a clinician can use to identify patients at high risk of SPLC. In addition, the mentored training program described in this proposal will accelerate Dr. Julie Wu’s development into an independent health services researcher. Her mentorship team includes Drs. Arya, Backhus, and Han- leaders in implementation science, thoracic oncology, and predictive modeling. From this proposal, the expected training outcomes will be 1) training in advanced modeling and database analytics and 2) training in qualitative methods and implementation science, which will help the PI achieve her research career goal of bridging the implementation gap between evidence and practice. Moreover, by completing this project, Dr. Wu will establish the training foundation and preliminary data for a CDA-2 application to identify current barriers to screening in high-risk Veteran lung cancer survivors and pilot an intervention aimed at overcoming those barriers. This project will have impact as Dr. Wu is part of the VA National Oncology Program and thus uniquely positioned to guide future expansion of the SPLC screening program across the national VA network.