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Algorithm to Identify Type 2 Diabetes Using Electronic Health Record and Self-Reported Data.

Ben T Varghese1,2, Marlene E Girardo3, Ruchi Gupta4

  • 1Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Metabolic Syndrome and Related Disorders
|April 7, 2025
PubMed
Summary
This summary is machine-generated.

An algorithm combining electronic health record and self-reported data accurately identifies type 2 diabetes (T2D). This method improves T2D classification in research cohorts, enhancing data reliability for studies.

Keywords:
algorithmbiobankdiabeteselectronic health recordself-report

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Area of Science:

  • Biomedical Informatics
  • Clinical Research

Background:

  • Accurate identification of participants with type 2 diabetes (T2D) is crucial for clinical research.
  • Relying solely on electronic health records (EHR) or self-reported data presents limitations in T2D classification accuracy.

Purpose of the Study:

  • To develop and validate an algorithm integrating EHR and self-reported data for precise identification of individuals with and without T2D.
  • To improve the accuracy of T2D case ascertainment in large biobank cohorts.

Main Methods:

  • Utilized data from the Mayo Clinic Biobank, including baseline questionnaires and EHR data (ICD codes, HbA1c, glucose, medications).
  • Developed an algorithm to classify participants into T2D, no T2D, "only self-reported T2D," and "only self-reported no T2D" categories.
  • Validated the algorithm's performance using manual chart reviews as the gold standard, calculating positive predictive value (PPV) and negative predictive value (NPV).

Main Results:

  • The algorithm classified 57,000 participants: 6,238 with T2D, 38,883 with no T2D, 757 with "only self-reported T2D," and 9,759 with "only self-reported no T2D."
  • Achieved high performance metrics: PPV of 96.0%, NPV of 100%, and overall accuracy of 99.5%.
  • Significant differences observed in age and sex distribution across classification groups.

Conclusions:

  • The developed algorithm demonstrates high accuracy and reliability for identifying individuals with and without T2D using combined EHR and self-reported data.
  • This validated algorithm offers a robust tool for T2D ascertainment in research settings, potentially applicable to other cohorts with linked EHR data.