Defining Quality of Care in Lung Cancer

背景：非小细胞肺癌(NSCLC)是退伍军人癌症相关死亡的主要原因。 随着计算机断层扫描筛查的实施，I期肺癌(肿瘤较少)的发病率 在退伍军人中)呈指数增长。I期非小细胞肺癌有可能通过以下方法治愈 手术被公认为治疗的黄金标准。对患有肺部疾病的退伍军人的护理存在很大的差异 癌症。护理方面的这些不一致直接与次优的短期和长期结果有关。缺乏 明确的指导方针是可变护理的重要决定因素。一些国家组织已经提出了 肺癌手术的质量措施(QMS)。然而，这些措施在很大程度上是制定出来的。 基于回溯的机构研究或专家意见。缺乏循证、有效的质量管理体系 仍然是一个关键的未得到满足的需求。为了解决这一关键的知识差距，我们将前瞻性地使用大型的 维护退伍军人健康管理局(VHA)数据库。这项提议的目标是开发一种模式，以 定义高质量的肺癌手术护理，并了解影响手术质量的因素。 意义：通过创建高质量的肺癌手术模式，与退伍军人和普通民众相关 对于人口，我们的建议直接涉及退伍军人事务部(VA)的护理质量优先事项 并将退伍军人事务部的数据转化为国宝。最近启动的退伍军人事务部-伙伴关系将增加获得 肺部筛查的目标是在可治愈的阶段检测出80%的肺癌。我们的研究重点是最佳治疗方法 对于早期肺癌来说，这是目前以及更重要的未来退伍军人群体的当务之急。 创新：我们的建议在概念和技术上都是创新的。概念创新涉及到 对可修改变量的整体考虑，以定义和影响高质量的医疗保健。技术上的 创新涉及利用预期维护的数据集实现一种独特的方法 模型开发和验证。 具体目标：目标1.确定肺癌手术的基于模型的质量衡量标准，并确定 对短期和长期结果的影响最大。我们假设，在候选QMS中， 我们的模型将确定与改善短期结果(可操作)相关的关键措施 发病率和死亡率)和长期生存。目的2.评估手术质量措施的依从性 并了解地理、患者、疾病和治疗相关因素在遵守 肺癌外科手术的质量措施。我们假设年轻的白人患者，肿瘤较小， 在城市设施接受治疗将与肺癌手术的QMS会议相关。 方法：在目标1中，利用VHA数据库，我们将检查遵守 以前提出的(例如手术类型、结节抽样范围)以及新的(例如手术延迟) 手术和短期结果(术后并发症、30天死亡率)和长期生存的QMS 使用回归模型。质量管理体系的相对重要性将通过排名进行评估。 在目标2中，我们将开发一个加权的、有效的QM遵守分数，范围从0(不遵守QMS)到 100(完全遵守质量管理体系)用于肺癌手术。我们将评估地理位置(例如，城市与 农村)、患者(种族、合并症)、疾病(例如肿瘤大小)和治疗(例如设施大小)相关的因素 坚持质量管理体系。 下一步/实施：我们的研究将定义“什么是高质量的肺癌手术”。为 在“如何优化高质量运营的可能性”的下一个逻辑步骤中，我们将提出干预措施 利用我们的研究结果和咨询委员会的意见处理重要的质量管理体系，该委员会代表 在肺癌、卫生政策和执行科学方面的专业知识。这些干预措施将得到完善和试点-- 在被提名为国家层面的政策变化之前，在我们的VISN 15未来的研究中进行了测试。

Background: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality in Veterans. With the implementation of computed tomography screening, the incidence of Stage I lung cancer (tumors less than 5 cm with no metastases) is increasing exponentially in Veterans. Stage I NSCLC is potentially curable with surgery as the recognized gold standard of therapy. Wide variations exist in the care of Veterans with lung cancer. These inconsistencies in care are directly linked to suboptimal short- and long-term outcomes. Lack of clear guidelines is an important determinant of variable care. A number of national organizations have proposed quality measures (QMs) for surgery in lung cancer. However, these measures have largely been developed based upon retrospective institutional studies or expert opinion. A lack of evidence-based, validated QMs remains a critical unmet need. To address this crucial gap in knowledge, we will use the large, prospectively maintained Veterans Health Administration (VHA) database. The goal of this proposal is to develop a model to define high-quality surgical care for lung cancer and understand factors impacting quality of surgery. Significance: By creating a model of high-quality surgery for lung cancer, relevant to Veterans and the general population, our proposal directly addresses the Department of Veterans Affairs (VA) priorities of quality of care and transforming VA data into a national treasure. The recently launched VA-Partnership to increase Access to Lung Screening aims to detect 80% of all lung cancers at a curable stage. Our study focuses on optimal therapy for early-stage lung cancer, a current and, even more importantly, future imperative for the Veteran population. Innovation: Our proposal is innovative both conceptually and technically. The conceptual innovation relates to the holistic consideration of modifiable variables to define and impact high quality healthcare. The technical innovation relates to the implementation of a unique approach utilizing a prospectively maintained dataset for model development and validation. Specific Aims: Aim 1. To identify model-based quality measures for surgery in lung cancer and determine which have the greatest impact on short, and long-term outcomes. We hypothesize that among the candidate QMs, our models will identify key measures that are associated with improved short-term outcomes (operative morbidity and mortality) and long-term survival. Aim 2. To evaluate adherence to quality measures for surgery and understand the contribution of geographic, patient-, disease-, and treatment-related factors in adherence to quality measures for surgery in lung cancer. We hypothesize that younger, white patients, with smaller tumors, treated at urban facilities will be associated with meeting QMs for surgery in lung cancer. Methodology: In Aim 1, utilizing the VHA database, we will examine the relationship between adherence to previously proposed (e.g. type of operation, extent of nodal sampling) as well as novel (e.g. delay in surgery) QMs for surgery and short-term outcomes (postoperative complications, 30-day mortality) and long-term survival using regression models. The relative importance of the QMs will be assessed by rank ordering. In Aim 2, we will develop a weighted, validated QM adherence score ranging from 0 (no adherence to QMs) to 100 (complete adherence to QMs) for lung cancer operations. We will evaluate geographic (e.g. urban versus rural), patient (race, comorbidities), disease (e.g. tumor size), and treatment (e.g. facility size) factors associated with adherence to QMs. Next Steps/Implementation: Our study will define “what constitutes a high-quality lung cancer operation”. For the next logical step of “how to optimize the likelihood of a high-quality operation”, we will propose interventions addressing important QMs with input from the results of our study and the advisory board, which represents expertise in lung cancer, health policy, and implementation science. These interventions will be refined and pilot- tested in our VISN 15 in a future study before being nominated for policy change at the national level.