Visualization of Sequential Treatments in Metastatic Breast Cancer

Abstract

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.

Treatment Sequencing Patterns and Associated Direct Medical Costs of Metastatic Breast Cancer Care in the United States, 2011 to 2021

“…35 In addition to treatment guidelines, tumor types, and prior treatments, insurance coverage, patient preferences, and the integration of a physician's personal experience with current scientific literature also influence treatment choices. [35][36][37][38] Tools are in development to rank the increasing number of regimens, such as the recently developed information theoretic network metaanalysis that was used to longitudinally rank HR-positive and ERBB2-negative treatments by efficacy. 39 We found a large variety in drug regimens used at a line and yearly level by subgroup, with some regimens being particularly infrequent.…”

“…A previous study from the linked SEER and Medicare database found that 56% of patients received an overall treatment sequence that less than 11 other patients also received. 35 In addition to treatment guidelines, tumor types, and prior treatments, insurance coverage, patient preferences, and the integration of a physician’s personal experience with current scientific literature also influence treatment choices. 35 , 36 , 37 , 38 Tools are in development to rank the increasing number of regimens, such as the recently developed information theoretic network meta-analysis that was used to longitudinally rank HR-positive and ERBB2-negative treatments by efficacy.…”

Rationale, Strengths, and Limitations of Real-World Evidence in Oncology: A Canadian Review and Perspective

“…For example, an analysis of the large real-world US SEER-Medicare database investigated treatment sequences in 6639 patients with metastatic breast cancer (MBC). 33 The heterogeneity of sequencing in real life was illustrated by their finding that 56% of these patients received a sequence that fewer than 11 other patients received. 33 This makes it challenging to extrapolate the results of RCTs with defined sequencing to the real world.…”

“… 33 The heterogeneity of sequencing in real life was illustrated by their finding that 56% of these patients received a sequence that fewer than 11 other patients received. 33 This makes it challenging to extrapolate the results of RCTs with defined sequencing to the real world. RWE can help clinicians and regulators effectively conceptualize treatment patterns and patient outcomes, improving patient care in oncology.…”

An Automated Line-of-Therapy Algorithm for Adults With Metastatic Non–Small Cell Lung Cancer: Validation Study Using Blinded Manual Chart Review

“…Extraction of LOT data from real-world transactional claims and electronic health records (EHRs) is essential for determining longitudinal SACT changes in real-world clinical care settings, but it is challenging because LOT information is often not clearly marked in structured data sets and therefore must be interpreted through clinical notes [6,9]. Researchers and clinicians use LOT information gathered retrospectively to determine the effectiveness of SACT regimens, identify trends in clinical practice patterns, identify eligible candidates for cancer trials, and conduct quality assurance to help ensure that patients receive optimal SACT [6,10,11]. Manual determination of LOT information for large numbers of patients is time consuming and often not feasible, prompting our own and others' searches for automated LOT algorithmic methods [6,[12][13][14][15].…”