Abstract

Approximately 1.2 million Veterans are eligible for lung cancer screening (LCS) annually. An estimated 25% (n=300,000) are projected to have non-cancer interstitial lung abnormalities (ILA). ILAs refer to the presence of radiographic abnormalities suggestive of early interstitial lung disease (ILD), a group of disorders of progressive lung scarring (i.e., fibrosis). Importantly, not all ILAs progress uniformly and certain radiographic and clinical features are associated with worse prognosis. However, there is currently no systematic method for Veterans with ILA detected on LCS to receive follow-up care. Because of a finite number of pulmonologists in the VA, the healthcare system will not be able to absorb 300,000 Veterans with ILA into specialty care. To balance finite resources while ensuring efficient access among Veterans at highest risk, novel techniques to identify, triage, and facilitate follow-up for Veterans with ILA on LCS are needed. Significance/Impact. The goal of this proposal is to develop a risk prediction model for ILA progression and mortality to inform the development of a follow-up care pathway embedded within operational workflows that will facilitate timely access to care for Veterans with ILA detected on LCS. This proposal addresses multiple VA research priorities including: (1) health systems research topic areas of organization and delivery, clinical management, and quality, (2) strategic methodology areas of implementation science, data science, engagement science, and (3) the quintuple aims of improving outcomes, increasing access, decreasing costs, and supporting a finite workforce. It also addresses the goals of the Promise to Address Comprehensive Toxics (PACT) Act, which makes access to ILD care, a new military service-connected disability, a priority. Innovation. The development of a risk prediction model for ILA progression is innovative. Leveraging lung texture analysis (LTA), a novel validated machine learning tool, to objectively quantify radiographic fibrosis and inform prediction modeling is innovative. Developing a follow-up care algorithm that integrates risk is innovative and will facilitate the care of a projected 300,000 Veterans with ILA annually while optimizing clinical efficiency. Specific Aims. Aim 1: Develop a risk prediction model to inform care triage among Veterans with ILA detected on LCS. Aim 2A: Establish actionable thresholds for follow-up care informed by clinical risk using a modified Delphi consensus process. Aim 2B: Conduct patient focus groups to integrate Veteran preference into communication of findings. Aim 3: Develop and pilot test a follow-up care pathway to facilitate care for Veterans with ILA and evaluate usability and acceptability of integration with LCS clinical workflows. Methodology. In Aim 1, we will identify a random sample of 2,000 patients with ILA detected on LCS between 2014 – 2017 and extract 5-year clinical and outcome data (progression, survival) from the electronic health record. Risk prediction models will characterize the radiographic and clinical features associated with progression (model 1) and mortality (model 2). In Aim 2, we will (a) convene a panel of 16 clinical experts through the Pulmonary Fibrosis Foundation Care Network for a 2-round modified Delphi panel to establish actionable thresholds and guidance for ILA follow-up care and (b) conduct patient focus groups to understand Veterans preference for communication of incidental findings guided by Forsey et al’s review of patient- physician communication. In Aim 3, we will develop a follow-up care pathway and conduct formative assessment to evaluate usability and acceptability of integration within clinical workflows. Next Steps/Implementation. The long-term goal of this CDA is to facilitate timely access to follow-up care for Veterans with ILA detected on LCS, prevent underuse or overuse of subspecialty care resources, and improve morbidity and mortality by developing a workflow for operational partners that optimizes clinical efficiency. Successful completion of the aims will enable a future implementation-effectiveness study evaluating the efficacy and impact of our care pathway under the purview of the LCS and Lung Precision Oncology Program. PUBLIC HEALTH RELEVANCE: Approximately 1.2 million Veterans are eligible for lung cancer screening (LCS) annually. An estimated 25% (n=300,000) are projected to have non-cancer interstitial lung abnormalities (ILA) consistent with early pulmonary fibrosis. There is no systematic method for Veterans with ILA detected on LCS to receive follow-up care. Because not all ILA’s progress uniformly, novel techniques that facilitate access to care while balancing healthcare system resources are needed. In partnership with the Lung Precision Oncology Program, we will optimize clinical efficiency by: (1) developing risk prediction models to inform ILA care triage, (2) establish actionable thresholds for follow-up care using a modified Delphi panel and conduct patient focus groups to integrate Veteran preference into communication of findings, (3) develop an ILA follow-up care pathway and evaluate usability and acceptability within clinical workflows. Successful completion of these aims will support Dr. Bhavika Kaul’s career development and facilitate care for up to 300,000 Veterans with ILA annually.

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DRA: Cancer, Lung Disorders, Health Systems Science

DRE: Data Science, Technology Development and Assessment, TRL - Applied/Translational

Keywords: Cancer, Technology Development

MeSH Terms: None at this time.