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. 2021 May:5:600-614.
doi: 10.1200/CCI.21.00031.

Treatment and Monitoring Variability in US Metastatic Breast Cancer Care

Affiliations

Treatment and Monitoring Variability in US Metastatic Breast Cancer Care

Jennifer L Caswell-Jin et al. JCO Clin Cancer Inform. 2021 May.

Abstract

Purpose: Treatment and monitoring options for patients with metastatic breast cancer (MBC) are increasing, but little is known about variability in care. We sought to improve understanding of MBC care and its correlates by analyzing real-world claims data using a search engine with a novel query language to enable temporal electronic phenotyping.

Methods: Using the Advanced Cohort Engine, we identified 6,180 women who met criteria for having estrogen receptor-positive, human epidermal growth factor receptor 2-negative MBC from IBM MarketScan US insurance claims (2007-2014). We characterized treatment, monitoring, and hospice usage, along with clinical and nonclinical factors affecting care.

Results: We observed wide variability in treatment modality and monitoring across patients and geography. Most women received first-recorded therapy with endocrine (67%) versus chemotherapy, underwent more computed tomography (CT) (76%) than positron emission tomography-CT, and were monitored using tumor markers (58%). Nearly half (46%) met criteria for aggressive disease, which were associated with receiving chemotherapy first, monitoring primarily with CT, and more frequent imaging. Older age was associated with endocrine therapy first, less frequent imaging, and less use of tumor markers. After controlling for clinical factors, care strategies varied significantly by nonclinical factors (median regional income with first-recorded therapy and imaging type, geographic region with these and with imaging frequency and use of tumor markers; P < .0001).

Conclusion: Variability in US MBC care is explained by patient and disease factors and by nonclinical factors such as geographic region, suggesting that treatment decisions are influenced by local practice patterns and/or resources. A search engine designed to express complex electronic phenotypes from longitudinal patient records enables the identification of variability in patient care, helping to define disparities and areas for improvement.

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Conflict of interest statement

Jennifer L. Caswell-JinResearch Funding: QED TherapeuticsUncompensated Relationships: NateraUncompensated Relationships: Color Genomics Alison CallahanConsulting or Advisory Role: Atropos Health LLCPatents, Royalties, Other Intellectual Property: I am named as an inventor on a patent pending for a search engine for patient data Douglas W. BlayneyLeadership: Artelo BiosciencesStock and Other Ownership Interests: Artelo Biosciences, MadorraConsulting or Advisory Role: Creare, Daiichi Sankyo, Embold Health, Lilly, Google, G1 Therapeutics, MerckResearch Funding: BeyondSpring Pharmaceuticals, CreareOpen Payments Link: https://openpaymentsdata.cms.gov/physician/728442 George W. SledgeLeadership: Syndax, Tessa TherapeuticsStock and Other Ownership Interests: Syndax, Tessa Therapeutics, PionyrConsulting or Advisory Role: Symphogen, Synaffix, Syndax, Verseau Therapeutics, Grail, AstraZenecaResearch Funding: Genentech/Roche, PfizerTravel, Accommodations, Expenses: Verseau Therapeutics, Tessa Therapeutics Nigam H. ShahLeadership: Prealize, Atropos HealthConsulting or Advisory Role: ApixioResearch Funding: Google Allison W. KurianResearch Funding: Myriad GeneticsOther Relationship: Ambry Genetics, Color Genomics, GeneDx/BioReference, Invitae, GenentechNo other potential conflicts of interest were reported.

Figures

FIG 1.
FIG 1.
ACE variables to retrieve the MBC patient cohort (10 lines). The variable mc retrieves the first occurrence of a time interval in an enrollee record where two metastatic cancer codes occurred within a week of each other, without requiring users to specify any notion of calendar time or have any knowledge of how enrollee records are represented in the source data. Queries to an equivalent relational database would require joins across three tables for each year of data and for both Medicare and commercial claims (each stored in separate tables), resulting in query and retrieval from a total of 3 × 7 × 2 = 42 tables. ACE, Advanced Cohort Engine; MBC, metastatic breast cancer.
FIG 2.
FIG 2.
Cohort selection. Flow diagram of selection process from MarketScan Database records. CPT, Current Procedural Terminology; ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; ICD, International Classification of Diseases; MBC, metastatic breast cancer.
FIG 3.
FIG 3.
ACE patient timelines. Timelines begin at time of initial secondary malignancy code (year 0) and end at end of record. Selected events, highlighted with distinct colors, are shown over 3 years for 50 patients from the cohort. The patient whose timeline is bracketed with asterisks received endocrine therapy in the first year after diagnosis of MBC and switched to chemotherapy in the second year after diagnosis when she was also diagnosed with visceral metastasis. ACE, Advanced Cohort Engine; CT, computed tomography; MBC, metastatic breast cancer; PET, positron emission tomography.
FIG 4.
FIG 4.
Treatment pathways for MBC. Sunburst diagrams illustrating treatment pathways for patients who received (A) endocrine therapy first (67% of cohort) or (B) chemotherapy first (33% of cohort). Endocrine therapies are shown in shades of blue, and chemotherapies are shown in shades of red. The innermost circles represent the first treatment in a sequence, the next outer circle the second treatment in a sequence, and so on. The most common first endocrine therapy treatment was an aromatase inhibitor. The most common first chemotherapy treatment was a taxane. The most common other endocrine therapies were tamoxifen and toremifene. The most common other single-agent chemotherapies were gemcitabine and vinorelbine. Pathways longer than (A) six for endocrine therapy–first or (B) five for chemotherapy-first patients (6.5% of all unique pathways; 1.3% of patients) are not shown, as they would not be clearly visible. MBC, metastatic breast cancer.
FIG 5.
FIG 5.
Multivariable logistic regression analysis to evaluate the association between each care event and patient, disease, and other factors. Five separate logistic regression models were fitted using as outcomes: first-recorded treatment after secondary malignancy code (chemotherapy v endocrine therapy), majority imaging modality (PET-CT v CT), imaging frequency (higher v lower than the median of every 81 days), tumor marker (CA 15-3 or CA 27-29) use (present v absent), and hospice use (present v absent). ORs and 95% CIs are displayed. Geographic regions: 1 New England, 2 Mid-Atlantic, 3 East North Central, 4 West North Central, 5 South Atlantic, 6 East South Central, 7 West South Central, 8 Mountain, and 9 Pacific. CT, computed tomography; OR, odds ratio; PET-CT, positron emission tomography-computed tomography.

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