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Identification of atypical pediatric diabetes mellitus cases using electronic medical records
Marcela F Astudillo1, William E Winter2, Liana K Billings3
1Texas Children's Hospital-Pediatric Diabetes & Endocrinology, Baylor College of Medicine Department of Pediatrics, Houston, Texas, USA marc_astudillo1@hotmail.com.
Insights
Electronic health records can identify children with atypical diabetes, including unknown types, type 2 diabetes under age 10, and autoantibody-negative type 1 diabetes. This aids in advancing precision diabetes care.
Area of Science:
- Pediatric Endocrinology
- Diabetes Research
- Health Informatics
Background:
- Establishing methods to identify atypical pediatric diabetes is crucial for targeted research.
- Electronic Medical Records (EMR) offer a potential resource for systematic case ascertainment.
- Current identification strategies for atypical diabetes in children are lacking.
Purpose of the Study:
- To develop and test strategies for systematically identifying atypical pediatric diabetes cases using EMR.
- To improve the detection of rare diabetes subtypes in children.
Main Methods:
- Two strategies were evaluated in a large US pediatric hospital.
- Strategy 1 involved a questionnaire-based EMR review for 100 youth with diabetes.
- Strategy 2 utilized electronic queries to identify phenotypes: unknown diabetes type, type 2 diabetes (T2D) diagnosed <10 years old, and autoantibody-negative type 1 diabetes (AbNegT1D).
Main Results:
- Strategy 1 identified 6% of atypical diabetes cases.
- Strategy 2 identified varying percentages for each phenotype: unknown type (1%), T2D <10 years old (6.6%), and AbNegT1D (5.6%).
- The identified pediatric diabetes population exhibited high racial and ethnic diversity.
Conclusions:
- Systematic EMR review can identify 1%-6.6% of atypical diabetes cases in diverse pediatric populations.
- Improved, unbiased identification methods are essential for advancing precision diabetes.
- These findings support the use of EMR for epidemiological studies in pediatric diabetes.
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 <10 years old and autoantibody-negative type 1 diabetes (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 <10 years old: n=64 (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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