Detection of Diabetes Status and Type in Youth Using Electronic Health Records: The SEARCH for Diabetes in Youth Study

Diabetes Care. 2020 Oct;43(10):2418-2425. doi: 10.2337/dc20-0063. Epub 2020 Jul 31.


Objective: Diabetes surveillance often requires manual medical chart reviews to confirm status and type. This project aimed to create an electronic health record (EHR)-based procedure for improving surveillance efficiency through automation of case identification.

Research design and methods: Youth (<20 years old) with potential evidence of diabetes (N = 8,682) were identified from EHRs at three children's hospitals participating in the SEARCH for Diabetes in Youth Study. True diabetes status/type was determined by manual chart reviews. Multinomial regression was compared with an ICD-10 rule-based algorithm in the ability to correctly identify diabetes status and type. Subsequently, the investigators evaluated a scenario of combining the rule-based algorithm with targeted chart reviews where the algorithm performed poorly.

Results: The sample included 5,308 true cases (89.2% type 1 diabetes). The rule-based algorithm outperformed regression for overall accuracy (0.955 vs. 0.936). Type 1 diabetes was classified well by both methods: sensitivity (Se) (>0.95), specificity (Sp) (>0.96), and positive predictive value (PPV) (>0.97). In contrast, the PPVs for type 2 diabetes were 0.642 and 0.778 for the rule-based algorithm and the multinomial regression, respectively. Combination of the rule-based method with chart reviews (n = 695, 7.9%) of persons predicted to have non-type 1 diabetes resulted in perfect PPV for the cases reviewed while increasing overall accuracy (0.983). The Se, Sp, and PPV for type 2 diabetes using the combined method were ≥0.91.

Conclusions: An ICD-10 algorithm combined with targeted chart reviews accurately identified diabetes status/type and could be an attractive option for diabetes surveillance in youth.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Age of Onset
  • Algorithms
  • Diabetes Mellitus / diagnosis*
  • Diabetes Mellitus / epidemiology
  • Diabetes Mellitus, Type 1 / diagnosis
  • Diabetes Mellitus, Type 1 / epidemiology
  • Diabetes Mellitus, Type 2 / diagnosis
  • Diabetes Mellitus, Type 2 / epidemiology
  • Electronic Health Records / statistics & numerical data*
  • Female
  • Humans
  • Male
  • Mass Screening / methods*
  • Predictive Value of Tests
  • United States / epidemiology
  • Young Adult

Associated data

  • figshare/10.2337/figshare.12558875