Work

Treatment and Monitoring Variability in US Metastatic Breast Cancer Care

Year: 2021

Type: article

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.

Source: JCO Clinical Cancer Informatics

DOI: 10.1200/cci.21.00031