Identification of Atypical Pediatric Diabetes Mellitus Cases Using Electronic Medical Records

Authors

Marcela F Astudillo
William E Winter
Liana K Billings
Raymond Kreienkamp
Ashok Balasubramanyam
Maria J Redondo
Mustafa Tosur

Publication Date

11-7-2024

Journal

BMJ Open Diabetes Research Care

DOI

10.1136/bmjdrc-2024-004471

PMID

39510599

PMCID

PMC11551976

PubMedCentral® Posted Date

11-7-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Male, Electronic Health Records, Child, Female, Adolescent, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Surveys and Questionnaires, Prognosis, MODY, Children, Pediatric Diabetes

Abstract

INTRODUCTION: There are no established methods to identify children with atypical diabetes for further study. We aimed to develop strategies to systematically ascertain cases of atypical pediatric diabetes using electronic medical records (EMR).

RESEARCH DESIGN AND METHODS: We tested two strategies in a large pediatric hospital in the USA. Strategy 1: we designed a questionnaire to rule out typical diabetes and applied it to the EMR of 100 youth with diabetes. Strategy 2: we built three electronic queries to generate reports of three atypical pediatric diabetes phenotypes: unknown type, type 2 diabetes (T2D) diagnosed(AbNegT1D).

RESULTS: Strategy 1 identified six cases (6%) of atypical diabetes (mean diagnosis age=11±2.6 years, 16.6% men, 33% non-Hispanic white (NHW) and 66.6% Hispanic). Strategy 2: unknown diabetes type: n=68 (1%) out of 6676 patients with diabetes; mean diagnosis age=12.6±3.3 years, 32.8% men, 23.8% NHW, 47.6% Hispanic, 25.4% African American (AA), 3.2% other. T2D(6.6%) out of 1142 patients with T2D; mean diagnosis age=8.6±1.6 years, 20.3% men, 4.7% NHW, 65.6% Hispanic, 28.1% AA, 1.6% other. AbNegT1D: n=38 (5.6%) out of 680 patients with new onset T1D; mean diagnosis age=11.3±3.8 years; 57.9% men, 50% NHW, 19.4% Hispanic, 22.3% AA, 8.3% other.

CONCLUSIONS: In sum, we identified 1%-6.6% of atypical diabetes cases in a pediatric diabetes population with high racial and ethnic diversity using systematic review of the EMR. Better identification of these cases using unbiased approaches may advance precision diabetes.

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