Comparative-Effectiveness of Pretreatment Lung Cancer Nodal Staging

治疗前肺癌淋巴结分期的比较有效性

基本信息

批准号：
10551866
负责人：
Farhood Farjah
金额：
$ 60.06万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-17 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10551866
关键词：
Address Adoption Adverse event Biopsy Cancer Patient Cancer Research Network Caring Case Mixes Characteristics Classification Clinical Data Databases Diagnostic Diagnostic Errors Disease Electronic Health Record Eligibility Determination Evaluation Expenditure Funding Goals Guidelines Health Expenditures Health Services Accessibility Hospitals Image Infrastructure Injury Investigation Learning Level of Evidence Life Link Machine Learning Malignant Neoplasms Malignant neoplasm of lung Medical Modeling National Cancer Institute Network Infrastructure Nodal Non-Small-Cell Lung Carcinoma Observational Study Operative Surgical Procedures Outcome Pathologic Patient Care Patient Selection Patient-Focused Outcomes Patients Patterns of Care Pilot Projects Practice Guidelines Procedures Process Recommendation Research Design Risk Risk Estimate Selection Criteria Selection for Treatments Serious Adverse Event Site Specialist Staging Survival Rate Techniques Testing Time Translating Uncertainty Unnecessary Surgery Variant Vital Status Work care costs chemotherapy cohort comparative effectiveness comparative effectiveness study cost data sharing design effectiveness evaluation follow-up high risk implementation strategy improved lung cancer screening lymph node biopsy lymph nodes models and simulation neoplasm registry novel novel strategies predictive modeling radiological imaging treatment optimization trial comparing tumor

Address Adoption Adverse event Biopsy Cancer Patient Cancer Research Network Caring Case Mixes Characteristics Classification Clinical Data Databases Diagnostic Diagnostic Errors Disease Electronic Health Record Eligibility Determination Evaluation Expenditure Funding Goals Guidelines Health Expenditures Health Services Accessibility Hospitals Image Infrastructure Injury Investigation Learning Level of Evidence Life Link Machine Learning Malignant Neoplasms Malignant neoplasm of lung Medical Modeling National Cancer Institute Network Infrastructure Nodal Non-Small-Cell Lung Carcinoma Observational Study Operative Surgical Procedures Outcome Pathologic Patient Care Patient Selection Patient-Focused Outcomes Patients Patterns of Care Pilot Projects Practice Guidelines Procedures Process Recommendation Research Design Risk Risk Estimate Selection Criteria Selection for Treatments Serious Adverse Event Site Specialist Staging Survival Rate Techniques Testing Time Translating Uncertainty Unnecessary Surgery Variant Vital Status Work care costs chemotherapy cohort comparative effectiveness comparative effectiveness study cost data sharing design effectiveness evaluation follow-up high risk implementation strategy improved lung cancer screening lymph node biopsy lymph nodes models and simulation neoplasm registry novel novel strategies predictive modeling radiological imaging treatment optimization trial comparing tumor

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项目摘要

ABSTRACT Our goal is to reduce diagnostic and treatment errors, improve survival, and increase the value of care for lung cancer patients by improving our ability to select patients who benefit from a pretreatment lymph node biopsy. Accurately determining whether cancer has spread to lymph nodes and the extent of spread (a process called nodal staging) is critical for appropriate treatment selection. Understaging can lead to omission of chemotherapy or unnecessary surgery. Overstaging can lead to unnecessary chemotherapy and omission of surgery. Diagnostic and treatment errors negatively impact survival. These errors commonly occur when using imaging alone for nodal staging. A biopsy can reduce the chances of error, but it can also result in rare, life- threatening adverse events. Each biopsy costs ~$5,000. Practice guidelines recommend selectively performing a biopsy when imaging findings suggest nodal disease. However, national biopsy rates are less than half of what they should be. Moreover, there is 25-fold facility-level variability not explained by access to care, case- mix, or clinician or facility characteristics. These findings, along with the low levels of evidence underlying guideline recommendations, suggest true clinical and scientific uncertainty over the indications for lymph node biopsy. We conducted a pilot study to better understand how well guideline recommendations select patients for biopsy and learned that guideline-concordant nodal staging selects all patients with true nodal disease for biopsy and two-thirds of patients without true nodal disease for biopsy. Additionally, we developed and validated an alternative risk-based nodal staging strategy that uses a prediction model to stratify and select patients for lymph node biopsy. Preliminary data show that it identifies nearly all patients with true nodal disease for biopsy but selects fewer patients without nodal true nodal disease for biopsy. However, the relationship between selection strategies for lymph node biopsy and patient outcomes remains unknown. We hypothesize that guideline-concordant nodal staging is associated with higher 5-year survival rates compared with guideline-discordant nodal staging (Aim I) and that risk-based nodal staging is equivalent to guideline- concordant nodal staging in terms of survival but superior in terms of lower biopsy-related adverse events and healthcare expenditures (Aim II). Testing these hypotheses will require ~4,000 patients; therefore, a trial is not feasible at this time. We will create a novel cohort of lung cancer patients using the Cancer Research Network infrastructure to conduct Aim I using an observational, comparative-effectiveness study design with advanced regression techniques and machine learning to minimize confounding. Additionally, we will use patient-level data from this cohort as model inputs in a comparative-effectiveness simulation model that we will develop to conduct Aim II. Findings from this study will lead to: 1) developing and testing implementation strategies designed to increase guideline-concordant nodal staging, 2) alternative guideline recommendations for nodal staging, and/or 3) justifying trials comparing outcomes between different nodal staging strategies.

抽象的我们的目标是减少诊断和治疗错误，提高生存率并增加肺部护理价值癌症患者通过提高我们选择从预处理淋巴结活检中受益的患者的能力。准确确定癌症是否扩散到淋巴结和扩散程度（一个称为的过程节点分期）对于适当的治疗选择至关重要。轻视可能导致省略化学疗法或不必要的手术。超级宣传可能会导致不必要的化学疗法和省略外科手术。诊断和治疗错误会对生存产生负面影响。使用这些错误时通常会发生单独进行成像进行淋巴结分期。活检可以减少错误的机会，但也可能导致罕见的生活 - 威胁不良事件。每个活检的费用约为5,000美元。练习指南建议有选择性地执行成像发现时的活检表明淋巴结疾病。但是，国家活检率不到一半他们应该是什么。此外，通过获得护理，病例 - 混合或临床医生或设施特征。这些发现，以及较低的证据基础指南建议，建议对淋巴结的适应症进行真正的临床和科学不确定性活检。我们进行了一项试点研究，以更好地了解指南建议如何选择患者为了进行活检并了解到指南符合淋巴结阶段会选择所有具有真正淋巴结疾病的患者活检和三分之二没有进行活检的患者。此外，我们开发了验证了一种基于风险的替代节点分期策略，该策略使用预测模型进行分层和选择淋巴结活检的患者。初步数据表明，它几乎识别所有真正淋巴结的患者活检的疾病，但选择没有淋巴结真实淋巴结疾病的患者进行活检。但是，淋巴结活检和患者结局的选择策略之间的关系仍然未知。我们假设指南协调性结节分期与较高的5年生存率相比有关有指南介绍节点登台（AIM I），基于风险的节点分期等同于指南 - 一致的结节分期在生存方面，但在活检相关的不良事件和医疗支出（AIM II）。测试这些假设将需要约4,000名患者；因此，审判不是此时可行。我们将使用癌症研究网络创建一个新的肺癌患者队列基础架构使用高级的观察性，比较效果研究设计进行目标I 回归技术和机器学习以最大程度地减少混杂。此外，我们将使用患者级该队列中的数据作为模型输入在比较效应模拟模型中，我们将开发为行为AIM II。这项研究的发现将导致：1）制定和测试实施策略旨在增加指南符合淋巴结阶段，2）nodal的替代指南建议分期和/或3）合理的试验，以比较不同节点分期策略之间的结果。