Comparison of Population Characteristics in Real-World Clinical Oncology Databases in the US: Flatiron Health, SEER, and NPCR

Abstract

Background and Objective The Surveillance, Epidemiology, and End Results Program (SEER) program and the National Program of Cancer Registries (NPCR), are authoritative sources for population cancer surveillance and research in the US. An increasing number of recent oncology studies are based on the electronic health record (EHR)-derived de-identified databases created and maintained by Flatiron Health. This report describes the differences in the originating sources and data development processes, and compares baseline demographic characteristics in the cancer-specific databases from Flatiron Health, SEER, and NPCR, to facilitate interpretation of research findings based on these sources. Methods Patients with documented care from January 1, 2011 through May 31, 2019 in a series of EHR-derived Flatiron Health de-identified databases covering multiple tumor types were included. SEER incidence data (obtained from the SEER 18 database) and NPCR incidence data (obtained from the US Cancer Statistics public use database) for malignant cases diagnosed from January 1, 2011 to December 31, 2016 were included. Comparisons of demographic variables were performed across all disease-specific databases, for all patients and for the subset diagnosed with advanced-stage disease. Results As of May 2019, a total of 201,570 patients with 19 different cancer types were included in Flatiron Health datasets. In an overall comparison to national cancer registries, patients in the Flatiron Health databases had similar sex and geographic distributions, but appeared to be diagnosed with later stages of disease and their age distribution differs from the other datasets. For variables such as stage and race, Flatiron Health databases had a greater degree of incompleteness. There are variations in these trends by cancer types. Conclusions These three databases present general similarities in demographic and geographic distribution, but there are overarching differences across the populations they cover. Differences in data sourcing (medical oncology EHRs vs cancer registries), and disparities in sampling approaches and rules of data acquisition may explain some of these divergences. Furthermore, unlike the steady information flow entered into registries, the availability of medical oncology EHR-derived information reflects the extent of involvement of medical oncology clinics at different points in the specialty management of individual diseases, resulting in inter-disease variability. These differences should be considered when interpreting study results obtained with these databases.

Real-World Treatment Patterns and Progression-Free Survival Associated with Anaplastic Lymphoma Kinase (ALK) Tyrosine Kinase Inhibitor Therapies for ALK+ Non-Small Cell Lung Cancer

“…We conducted this retrospective study using the Flatiron Health database: a demographically and geographically diverse longitudinal deidentified database derived from data obtained from electronic health records (EHRs) in the U.S. [20, 21]. The database includes information from more than 265 cancer clinics (approximately 800 sites of care), representing more than 2 million U.S. patients with cancer available for analysis.…”

Characterization of a Real-World Response Variable and Comparison with RECIST-Based Response Rates from Clinical Trials in Advanced NSCLC

“…The Flatiron Health database is a nationwide longitudinal, de-identified EHR-derived database comprised of de-identified patient-level structured and unstructured data, curated via technology-enabled abstraction [10]. During the study period, this database included deidentified data from approximately 280 US cancer clinics (ca.…”

“…We considered only the assessments of response that followed radiographic imaging tests performed to evaluate disease burden, and the dates of those assessments were marked as ''assessment time points.'' This information was abstracted from the EHR by trained professionals [10].…”

Development of a “meta-model” to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support

“…We defined patient cohorts from 3 subsets of the nationwide Flatiron Health electronic health record–derived de-identified database 21 : (1) metastatic CRC, (2) advanced non-small cell lung cancer, and (3) metastatic breast cancer. For each patient in our cohort, we extracted and assembled outcome data (time surviving after the date of advanced diagnosis) and selected clinical features that were commonly available and that we thought might help to predict prognosis (“predictors”).…”