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

为接受手术的退伍军人提供以患者为中心的多学科护理（PAUSE）：一项混合 1 临床有效性实施干预试验

• 批准号: 10675316
• 负责人: Shipra Arya
• 金额: --
• 依托单位: VETERANS ADMIN PALO ALTO HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10675316
• 关键词: Acceleration; Address; Adherence; Adopted; African American population; Area; Assessment tool; Award; Biopsy; Calibration; Cancer Patient; Cancer Survivor; Cancer Survivorship; Caring; Cessation of life; Clinic; Clinical; Clinical effectiveness; Computerized Medical Record; Consolidated Framework for Implementation Research; Data; Databases; Decision Making; Development; Diagnosis; Disparity; Early Diagnosis; Early identification; Equity; Foundations; Future; Goals; Harm Reduction; Health; Health Care Costs; Health Services; Health Services Research; Histology; Hybrids; Individual; Institution; Intervention; Intervention Trial; Interview; K-Series Research Career Programs; Longevity; Malignant neoplasm of lung; Mentors; Mentorship; Methods; Mission; Modeling; Oncology; Operative Surgical Procedures; Outcome; Outcomes Research; Parents; Patients; Performance; Pilot Projects; Population; Positioning Attribute; Predictive Cancer Model; Program Description; Provider; Qualitative Methods; Radiation exposure; Recurrence; Research; Research Personnel; Risk; Risk Assessment; Screening for cancer; Second Primary Cancers; Services; Site; Smoking History; Structure; Survivors; Thoracic Oncology; Training; Training Programs; Translating; United States Department of Veterans Affairs; Update; Veterans; Veterans Health Administration; Work; acceptability and feasibility; advanced analytics; cancer risk; cancer therapy; career; clinical care; clinical decision support; clinical practice; cohort; demographics; disparity reduction; evidence base; experience; frailty; high risk; implementation intervention; implementation science; implementation strategy; improved; innovation; lung cancer screening; military veteran; mortality; mortality risk; multidisciplinary; novel strategies; oncology program; patient oriented; patient population; precision oncology; predictive modeling; prevent; research and development; risk prediction model; risk stratification; screening; screening program; standard of care; success; support tools; survivorship; tool; uptake

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.

该项目的长期目标是通过开发和提高退伍军人的健康和寿命 实施循证筛查策略以预防第二原发肺死亡 肺癌幸存者的癌症（SPLC）。这很重要，因为 SPLC 不同于 复发是幸存者的主要死亡风险之一。尽管它很重要，但有 对于诸如哪些患者需要进行 SPLC 筛查之类的基本问题，没有基于证据的护理标准 以及持续多长时间。随着治疗和筛查的改进以及肺癌幸存者数量的增加 增加，迫切需要研究干预措施以实施基于证据的 对癌症幸存者进行癌症筛查。为了满足这一需求，我们建议采用证据- 基于策略越来越多地用于初始原发性肺癌（IPLC）筛查、风险 分层。风险分层显着改善了 IPLC 筛查，增加了 筛查效率，同时还控制不断膨胀的医疗成本并减少差异。 为了实现 SPLC 的类似改进，我们的团队开发并验证了预测 根据 SPLC 风险对肺癌幸存者进行分层的模型，现已在以下国家的临床环境中进行试点 这个项目。我们的项目是创新的，因为它是第一个（据我们所知）实施风险的项目 SPLC 分层。我们提案的目标是制定一个针对退伍军人的、 差异敏感的临床决策支持工具，可根据临床中的 SPLC 风险对退伍军人进行分层。我们的 中心假设是 1) 我们的模型可以准确识别 SPLC 高风险退伍军人 2) 在 VA 临床环境中识别高风险患者是可行的。为了实现 根据研究目标，将追求以下具体目标。目标 1 将验证并调整我们的 之前利用患者人口统计数据为退伍军人开发了 SPLC 风险预测模型， 吸烟史、IPLC 治疗、分期和组织学源自 VA EMR。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 混合方法方法，重点关注提供商决策和结构性或系统性 设施层面的影响。目标 3 将 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 吸收。本提案成功完成后，预期研究成果为 1) 为退伍军人提供更新的最佳 SPLC 风险预测模型，以及 2) 可行的临床 基于该模型的决策支持工具，临床医生可以使用它来识别高危患者 SPLC 的风险。此外，本提案中描述的指导培训计划将 加速朱莉·吴博士发展成为一名独立的健康服务研究员。她 导师团队包括博士。 Arya、Backhus 和 Han——实施科学领域的领导者， 胸部肿瘤学和预测建模。根据该提案，预期的培训成果 将是 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，这将帮助 PI 实现她的研究职业目标 缩小证据与实践之间的实施差距。此外，通过完成此 项目中，吴博士将为CDA-2建立训练基础和初步数据 应用程序以确定当前高风险退伍军人肺癌幸存者筛查的障碍 并试行旨在克服这些障碍的干预措施。该项目将产生如下影响 吴博士是 VA 国家肿瘤学计划的成员，因此具有独特的优势来指导 未来 SPLC 筛查计划将扩展到全国 VA 网络。

项目成果

Optimizing Lung Cancer Screening With Risk Prediction: Current Challenges and the Emerging Role of Biomarkers.

通过风险预测优化肺癌筛查：当前的挑战和生物标志物的新兴作用。

• DOI: 10.1200/jco.23.01060
• 发表时间: 2023
• 期刊: Journal of clinical oncology : official journal of the American Society of Clinical Oncology
• 作者: Wu,JulieTsu-Yu;Wakelee,HeatherA;Han,SummerS
• 通讯作者: Han,SummerS