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Algorithmic identification of atypical diabetes in electronic health record (EHR) systems

Author

Listed:
  • Sara J Cromer
  • Victoria Chen
  • Christopher Han
  • William Marshall
  • Shekina Emongo
  • Evelyn Greaux
  • Tim Majarian
  • Jose C Florez
  • Josep Mercader
  • Miriam S Udler

Abstract

Aims: Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review. Methods: Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. “Typical” T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional “branch algorithms,” relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information. Results: Of 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P

Suggested Citation

  • Sara J Cromer & Victoria Chen & Christopher Han & William Marshall & Shekina Emongo & Evelyn Greaux & Tim Majarian & Jose C Florez & Josep Mercader & Miriam S Udler, 2022. "Algorithmic identification of atypical diabetes in electronic health record (EHR) systems," PLOS ONE, Public Library of Science, vol. 17(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0278759
    DOI: 10.1371/journal.pone.0278759
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    References listed on IDEAS

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    1. Tian Ge & Chia-Yen Chen & Yang Ni & Yen-Chen Anne Feng & Jordan W. Smoller, 2019. "Polygenic prediction via Bayesian regression and continuous shrinkage priors," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
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