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Generalizability of kidney transplant data in electronic health records — The Epic Cosmos database vs the Scientific Registry of Transplant Recipients

Michal Mankowski, Sunjae Bae, Alexandra T. Strauss, Bonnie E. Lonze, Babak J. Orandi, Darren Stewart, Allan B. Massie, Mara McAdams‐DeMarco, Eric K. Oermann, Marlena Habal, Eduardo Iturrate, Sommer E. Gentry, Dorry L. Segev, David A. Axelrod

2024American Journal of Transplantation16 citationsDOIOpen Access PDF

Abstract

Developing real-world evidence from electronic health records (EHR) is vital to advancing kidney transplantation (KT). We assessed the feasibility of studying KT using the Epic Cosmos aggregated EHR data set, which includes 274 million unique individuals cared for in 238 US health systems, by comparing it with the Scientific Registry of Transplant Recipients (SRTR). We identified 69 418 KT recipients who underwent transplants between January 2014 and December 2022 in Cosmos (39.4% of all US KT transplants during this period). The demographics and clinical characteristics of recipients captured in Cosmos were consistent with the overall SRTR cohort. Survival estimates were generally comparable, although there were some differences in long-term survival. At 7 years posttransplant, patient survival was 80.4% in Cosmos and 77.8% in SRTR. Multivariable Cox regression showed consistent associations between clinical factors and mortality in both cohorts, with minor discrepancies in the associations between death and both age and race. In summary, Cosmos provides a reliable platform for KT research, allowing EHR-level clinical granularity not available with either the transplant registry or health care claims. Consequently, Cosmos will enable novel analyses to improve our understanding of KT management on a national scale.

Topics & Concepts

EPICMedicineGeneralizability theoryKidney transplantRenal transplantDatabaseHealth recordsElectronic health recordKidney transplantationInternal medicineTransplantationHealth careStatisticsLiteratureComputer scienceArtEconomic growthEconomicsMathematicsMachine Learning in HealthcareRenal Transplantation Outcomes and TreatmentsChronic Disease Management Strategies