PURPOSE Treatment sequencing of metastatic breast cancer (MBC) is heterogeneous. The primary objective of this study was to develop a visualization technique to understand population-level treatment sequencing for MBC. Secondary outcomes were to describe the heterogeneity of MBC treatment sequencing, as measured by the proportion of patients with a rare sequence, and to generate hypotheses about the impact of sequencing on overall survival. METHODS This retrospective review evaluated treatment sequencing for patients with MBC in the SEER–Medicare database. Patients with either de novo MBC or International Classification of Diseases, Ninth Revision, diagnosis codes for secondary metastasis (197.XX-198.XX) on two separate dates, excluding breast (198.81, 198.82, 198.2) and lymph nodes (196.XX), were included. Complete Medicare Parts A, B, and D coverage was required. A treatment sequence that fewer than 11 patients received was considered rare. A graphic was created with each nonrare treatment-sequence grouping on the y-axis and time on the x-axis. Bars representing time on hormonal therapy, chemotherapy, human epidermal growth factor receptor 2–targeted therapy, and other targeted therapies were color coded. Kaplan-Meier–like curves were overlaid on treatment maps, using estimated median survival for each sequence. RESULTS Of 6,639 patients with MBC, 56% received a treatment sequence that fewer than 11 other patients received, with 2,985 other unique, rare sequences were identified. Sequence visualization demonstrated differential survival, with longer median survival for those initially receiving hormonal therapy. The median time receiving initial treatment was similar for patients receiving first-line chemotherapy. CONCLUSION Treatment-sequence visualization can enhance the capacity to effectively conceptualize treatment patterns and patient outcomes.
PURPOSE Sequential drug treatments in metastatic breast cancer (MBC) are disparate. Clinical trial data includes limited reporting of treatment context, primarily including the number of prior therapies. This study evaluates the relationship between prior treatment time, prior lines of treatment, and survival using a novel visualization technique coupled with statistical analyses. PATIENTS AND METHODS This retrospective cohort study used a nationwide, de-identified electronic health record–derived database to identify women with hormone receptor–positive, human epidermal growth factor receptor 2–negative MBC diagnosed in 2014 who subsequently received paclitaxel. Images were created, with individual patients represented on the y-axis and time, on the x-axis. Specific treatments were represented by colored bars, with Kaplan-Meier curves overlaying the image. Separate images assessed progression-free survival and overall survival (OS). Hazard ratios (HRs) and 95% CIs from Cox proportional hazards models evaluated the association between prior treatment time and OS. RESULTS Of 234 patients, median survival from first paclitaxel administration was 20 months (interquartile range, 8-53 months). An inverse relationship was observed between OS after paclitaxel and timing of administration. In adjusted models, each year on treatment prior to paclitaxel was associated with a 16% increased hazard of death after paclitaxel (HR, 1.16; 95% CI, 1.05 to 1.29). CONCLUSION OS after a specific treatment is dependent on when a drug is given in the disease context, highlighting the potential for an overall OS benefit to be observed on the basis of treatment timing. Prior time on treatment should be considered as a stratifying factor in randomized trials and a confounding factor when examining survival in observational data.
317 Background: Electronic health record (EHR) databases are a promising platform for clinical research using real-world data. However, information on potential limitations of these data sources is lacking. We sought to understand how data visualization might be used to identify data inconsistencies and the applicability of previously validated claims-based algorithms used to identify patients with metastatic breast cancer (MBC). Methods: This retrospective study utilized ASCO’s CancerLinQ Discovery database derived from EHR data. Subjects included women ≥18 years treated for MBC diagnosed ≥1980. Subjects with MBC were identified using two billing codes for metastasis on separate dates following primary breast cancer diagnosis. Treatment course sequences were visualized. Patients were represented by a horizontal bar on the Y-axis. Treatments were displayed using colored bars (blue: chemotherapy, red: endocrine therapy, green: HER2 targeted, orange: novel therapy) with time of treatment on the X-axis. Visualizations were qualitatively evaluated, and treatment patterns inconsistent with clinical practice were identified. Results: We identified 4,760 women treated for MBC using billing codes for primary breast cancer diagnosis and distant metastasis. Most patients (96%) had a primary breast cancer diagnosed in 2000 later. Treatment patterns inconsistent with clinical practice identified using the visualization technique included: 1% of patients received adjuvant chemotherapy continuously for ≥1.5 years, suggesting missed coding for metastatic disease; 5% of patients did not receive any treatment in the year following metastasis, suggesting the billing code may have been used in workup and not for confirmed metastatic disease. Among patients with MBC, 50% identified as HR+ across all records had not received hormone therapy, while 39% identified as HR- across all records received hormone therapy. Conclusions: Because previously validated algorithms may not translate well to EHR databases, quality auditing should always be performed. The proposed data visualization can be used for improving algorithms, qualitatively identifying errors, and avoiding biased or inaccurate results.
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