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Related Experiment Video

Updated: Mar 29, 2026

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Immunocompromised Status Definition in Observational Studies Using Electronic Health Records: A Scoping Review and a

Judit Riera-Arnau1,2, Nicoletta Luxi1,3, Fabio Riefolo4

  • 1Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), Utrecht, the Netherlands.

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Summary
This summary is machine-generated.

Identifying immunocompromised individuals in electronic health records (EHRs) is challenging. This study developed a modular phenotype algorithm using diagnostic, therapeutic, and procedural data to improve identification in EHR databases.

Keywords:
diagnostic codeimmunocompromisedimmunodeficiencyimmunosuppressantphenotypereal‐world data

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Area of Science:

  • Immunology
  • Health Informatics
  • Epidemiology

Background:

  • Immunocompromised individuals have impaired immune function due to various congenital or acquired conditions.
  • Translating clinical definitions of immunocompromised status into machine-readable algorithms for electronic health records (EHRs) is complex due to transient states and data variability.
  • Existing definitions in epidemiological studies often focus on specific diseases or treatments.

Purpose of the Study:

  • To conduct a scoping review of immunocompromised status definitions in MEDLINE-indexed studies.
  • To develop a machine-readable phenotype algorithm for identifying immunocompromised populations in EHR data.
  • To address challenges in defining immunocompromised status for epidemiological research.

Main Methods:

  • Conducted a scoping review of MEDLINE citations focusing on epidemiologic and pharmacoepidemiologic studies.
  • Extracted data on conditions and medications, categorizing them into seven groups (e.g., genetic conditions, malignancies, immunosuppressive drugs).
  • Developed a modular phenotype algorithm integrating diagnostic, therapeutic, and procedural data.

Main Results:

  • Included 56 out of 137 reviewed studies.
  • Commonly cited diagnoses were HIV/AIDS and organ transplantation.
  • Methotrexate, corticosteroids, TNF-alpha inhibitors, and calcineurin inhibitors were frequently used drugs to define immunocompromised status.

Conclusions:

  • A modular phenotype algorithm was developed to identify immunocompromised populations in EHR data.
  • The algorithm combines diverse data types (diagnostic, therapeutic, procedural) for broader applicability.
  • The algorithm can be applied across various data sources, settings, and research questions, with future validation needed.