Using real-world data (RWD) from an integrated platform for rapid analysis of patients with cancer with and without COVID-19 across distinct health systems

Introduction: Reports suggest worsened outcomes in patients with cancer (pts) and COVID-19 (Cov), varying by geography and local peak dynamics. We describe characteristics and clinical outcomes of pts with and without Cov. Methods: RWD at 2 Midwestern health systems from the Syapse Learning Health Network were used to identify adults with active cancer (AC) or past history of cancer (PHC). AC pts were identified by encounters with ICD-10 code for malignant neoplasm or receipt of an anticancer agent within 12 months prior to February 15, 2020; PHC pts were identified by encounters with an active cancer code from May 15, 2015 to February 15, 2019 and no receipt of anticancer therapy within the prior 12 months. Cov was defined by diagnostic codes and laboratory results from February 15 to May 13, 2020. Comorbidities were assessed prior to February 15, 2020; hospitalizations (hosp), invasive mechanical ventilation (IMV), and all-cause mortality (M) were assessed from February 15 to May 27, 2020. Results: We identified 800 pts with Cov (0.5%) out of a total of 154,585 pts with AC or PHC. Compared to AC pts without Cov (AC WO, 39,402), AC pts with Cov (AC Cov, 388) were more likely to be non-Hispanic Black (NHB, 39% vs. 9%), have renal failure (RF, 24% vs. 12%), cardiac arrhythmias (33% vs. 19%), congestive heart failure (CHF, 16% vs. 8%), obesity (19% vs. 14%), pulmonary circulation disorder (PCD, 9% vs. 4%), and a zip code with median annual household income (ZMI) Conclusion: In this rapid characterization from RWD, pts with Cov have higher rates of pre-existing cardiopulmonary/vascular and renal conditions and increased risk of hospitalization, IMV, and mortality than pts without Cov. Higher Cov risk and worse outcomes in NHB and lower-income pts suggest health care disparities. Whether these outcomes are due to comorbidities or acute sequelae merits further study, as does investigation of alternative definitions for real-world populations and outcomes. Citation Format: Shirish M. Gadgeel, Michael A. Thompson, Monika A. Izano, Clara Hwang, Tom Mikkelsen, James L. Weese, Frank M. Wolf, Andrew Schrag, Sheetal Walters, Harpreet Singh, Jonathan Hirsch, Thomas D. Brown, Paul G. Kluetz. Using real-world data (RWD) from an integrated platform for rapid analysis of patients with cancer with and without COVID-19 across distinct health systems [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S10-02.