Work

Immunocompromised Status Definition in Observational Studies Using Electronic Health Records: A Scoping Review and a Proposal for a Phenotype Identification Algorithm

Year: 2026

Type: article

Abstract: Immunocompromised individuals experience an impaired immune function due to conditions that might be either congenital or acquired over the course of their lives. Epidemiological studies often rely on clinical definitions which, in some cases, benefit from being translated into machine-readable algorithms for application to electronic health records (EHRs) databases. The transient nature of certain immunocompromised states and the variability of phenotypes, definitions, coding practices, and data availability entangle this operation. To address these challenges, we conducted a scoping review of existing immunocompromised status definitions in MEDLINE, focusing on epidemiologic and pharmacoepidemiologic studies involving immunocompromised populations. Data extraction was guided by clinical experts, categorizing conditions and medications into seven categories: genetic/hereditary conditions, infectious diseases, malignancies and chemotherapy, organ and stem-cell transplantations, severe systemic conditions, immunosuppressive drugs, and autoimmune conditions associated with immunosuppressant use. Out of 137 citations, 56 studies were included. Most of the studies focused on a particular disease or therapeutic area. Frequently cited diagnoses included HIV/AIDS (17.9%) and organ transplantation (14.2%). Methotrexate, corticosteroids, TNF-alpha inhibitors, and calcineurin inhibitors were the most common drugs used to define immunocompromised status. Building on this review and expert opinion, we developed a phenotype algorithm that combines diagnostic, therapeutic, and procedural data in a modular way to identify immunocompromised populations in EHR data sources. The proposed phenotype algorithm can be applied across diverse data sources, settings and research questions. Future research should test its applicability across heterogeneous EHR data sources.

Source: Pharmacoepidemiology and Drug Safety

DOI: 10.1002/pds.70362