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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：
10365806
负责人：
Farhood Farjah
金额：
$ 66.57万
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/10365806
关键词：
Address Adoption Adverse event Biopsy Cancer Patient Cancer Research Network Caring Case Mixes Characteristics Clinical Data Databases Diagnostic Diagnostic Errors Disease Electronic Health Record Evaluation Expenditure Funding Goals Guidelines Health Expenditures Health Services Accessibility Hospitals Image Infrastructure Injury Investigation Lead 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 Suggestion Survival Rate Techniques Testing Time Translating Uncertainty Unnecessary Surgery Variant Vital Status Work base 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

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.

摘要