OBJECTIVES: Current approaches to malignancy risk-stratification for incidental PNs involve significant uncertainty and may result in unnecessary invasive procedures. This study aimed to estimate the potential financial impact to a health system through the integration of a blood-based auto-antibody (AAb) test and a blood-based integrated classifier (IC) test to reclassify risk of malignancy in incidental PNs.

METHODS: An Excel-based economic impact model was developed to evaluate use and impact of two blood-based tests for PN diagnosis and follow-up within a health system over a 1-year time horizon. Use of the tests within a health system was compared to standard of care (without the blood-based tests). Model inputs included those related to pre-test risk of lung cancer, risk classification with and without the blood-based tests, utilization of diagnostic procedures for PNs and lung cancer (e.g., imaging and invasive procedures informed by current diagnostic practices based on patient risk classification), and costs of procedures. Assumptions were made surrounding system workflows to estimate patient capacity and financial outcomes.

RESULTS: In a hypothetical health system with 1,000,000 patients, approximately 14.9% more patients were routed to CT scans while 29.4% fewer patients were routed to PET scans. An estimated 9.9% more biopsies were performed while surgeries were reduced by 11.6%. Use of the two blood-based tests would potentially increase diagnostic capacity, which would therefore lead to an increase in total reimbursement.

CONCLUSIONS: The use of minimally invasive blood-based tests to reclassify PN risk of malignancy in a health system demonstrates improved efficiencies within a lung nodule program. By reducing unnecessary invasive procedures such as biopsy and/or surgery, patients are rerouted to less invasive monitoring through imaging. Overall, integration of the tests generates more total revenue for a health system through an increased capacity impact.