Abstract

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.

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DRA: Lung Disorders, Cancer

DRE: Treatment - Comparative Effectiveness, TRL - Applied/Translational

Keywords: Best Practices, Clinical Performance Measures, Quality of Care

MeSH Terms: None at this time.