Abstract and Introduction
Purpose: Time from diagnosis to treatment has been associated with worse survival outcomes in non–small-cell lung cancer (NSCLC). However, little is known about the impact of delay in time to diagnosis. We aimed to evaluate the impact of time from radiographic suspicion to histologic diagnosis on survival outcomes using the US SEER-Medicare population database.
Methods: We identified patients from the SEER-Medicare data set diagnosed with any stage NSCLC between January 1, 2011, and December 31, 2015, who received stage-appropriate treatment and had a computed tomography scan within 1 year of diagnosis. Time to confirmation was determined as the interval between most recent computed tomography imaging and date of histologic diagnosis. Our primary outcome was overall survival (OS).
Results: In total, 10,824 eligible patients were identified. The median time to confirmation was 20 (range 0–363) days. Using multivariate Cox regression models, longer time to confirmation was associated with improved OS in all comers driven by stage IV patients after adjustment for age, sex, diagnosis year, histology, and comorbidity index. In a separate landmark analysis excluding patients deceased within 6 months of diagnosis, the association between time to diagnosis and survival was no longer evident.
Conclusion: Time to confirmation of NSCLC was inversely associated with OS in this US SEER population study. This association was lost when patients deceased within 6 months of diagnosis were excluded, suggesting that retrospective registry-claims databases may not be the optimal data source to study time to diagnosis as a quality metric because of the unaccounted confounding effects of tumor behavior. Prospective evaluations of clinically enriched data sources may better serve this purpose.
As the United States places increasing emphasis on quality improvement in health care, there has been a growing interest in defining high-quality and timely cancer care.[1,2] Despite lung cancer remaining the leading cause of cancer-related mortality in the United States, quality metrics in non–small-cell lung cancer (NSCLC) have lagged behind other common cancer types, including breast and colon cancers.[1,3] One challenge in improving quality care in NSCLC is defining timeliness of care. Work has been done demonstrating significant variability in time from first suspicion to treatment initiation.[2,4] Previous studies demonstrated associations between prolonged time from diagnosis to treatment initiation and inferior patient outcomes in early- and advanced-stage NSCLC.[5–9] Delays from histologic diagnoses to treatment have been increasing for all major cancer types, and although some degree of variation exists across institution types in time to treatment, previous research has clearly identified sociodemographic predictors of such delays.[4,7,10,11]
Although previous studies have evaluated the impact of delay from diagnosis to treatment initiation in NSCLC, little is known about the time interval from a suspected diagnosis of lung cancer to histologic confirmation and how variations in that interval affect survival outcomes among patients who receive appropriate oncologic therapy. To improve care delivery, providers need further insights into the magnitude of variation in this time interval and the extent to which delays in diagnostic workup independently affect survival. We hypothesize that prolonged intervals from a suspected diagnosis of NSCLC to histologic confirmation (time to diagnosis) is associated with inferior overall survival (OS) across all disease stages regardless of timely treatment initiation.
Using the population-based SEER registry linked to Medicare claims, we conducted a retrospective cohort study to pursue the following goals: (1) to characterize the time intervals from radiographic suspicion to histologic confirmation of NSCLC and (2) to test the associations of time intervals from radiographic suspicion to histologic confirmation with OS, after accounting for patient confounding characteristics and receipt of oncologic treatments.
J Oncol Pract. 2022;18(6):e877-e885. © 2022 American Society of Clinical Oncology