Related Experiment Video
Updated: Dec 10, 2025
A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
Published on: July 5, 2022
An Iterative Process for Identifying Pediatric Patients With Type 1 Diabetes: Retrospective Observational Study
Heather Lynne Morris1, William Troy Donahoo2, Brittany Bruggeman2
1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States.
Insights
A new computable phenotype accurately identifies type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children. This method improves efficiency and cost-effectiveness for identifying pediatric diabetes patients for research.
Area of Science:
- Pediatric Endocrinology
- Computational Health Informatics
- Diabetes Research
Background:
- Rising incidence of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in pediatric populations.
- Current methods for diagnosing pediatric diabetes are labor-intensive and costly.
Purpose of the Study:
- To develop a computable phenotype for precise and efficient identification of diabetes in children.
- To differentiate between T1DM and T2DM in pediatric patients.
Main Methods:
- Retrospective study using electronic health records from the University of Florida Health Integrated Data Repository.
- Development and validation of a computable phenotype to identify pediatric diabetes cases.
- Manual review of medical records by endocrinology specialists to confirm diagnoses.
Main Results:
- The computable phenotype demonstrated high accuracy with a positive predictive value of 94.7% and sensitivity of 96.9%.
- Specificity was 95.8% and negative predictive value was 97.6%.
- Successfully identified 128 cases of T1DM and 35 cases of T2DM among 295 reviewed records.
Conclusions:
- A validated computable phenotype enables accurate and efficient identification of T1DM in pediatric patients.
- This tool offers a cost-effective solution for researchers to identify patient cohorts for intervention studies.
- Facilitates improved ease and accuracy in selecting participants for future diabetes research.
Background:
The incidence of both type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children and youth is increasing. However, the current approach for identifying pediatric diabetes and separating by type is costly, because it requires substantial manual efforts.
Objective:
The purpose of this study was to develop a computable phenotype for accurately and efficiently identifying diabetes and separating T1DM from T2DM in pediatric patients.
Methods:
This retrospective study utilized a data set from the University of Florida Health Integrated Data Repository to identify 300 patients aged 18 or younger with T1DM, T2DM, or that were healthy based on a developed computable phenotype. Three endocrinology residents/fellows manually reviewed medical records of all probable cases to validate diabetes status and type. This refined computable phenotype was then used to identify all cases of T1DM and T2DM in the OneFlorida Clinical Research Consortium.
Results:
A total of 295 electronic health records were manually reviewed; of these, 128 cases were found to have T1DM, 35 T2DM, and 132 no diagnosis. The positive predictive value was 94.7%, the sensitivity was 96.9%, specificity was 95.8%, and the negative predictive value was 97.6%. Overall, the computable phenotype was found to be an accurate and sensitive method to pinpoint pediatric patients with T1DM.
Conclusions:
We developed a computable phenotype for identifying T1DM correctly and efficiently. The computable phenotype that was developed will enable researchers to identify a population accurately and cost-effectively. As such, this will vastly improve the ease of identifying patients for future intervention studies.
Related Concept Videos
01:22Diabetes Mellitus: Overview and Type I Subtype
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
01:22Diabetes Mellitus: Type 2 and Gestational
01:11Observational Studies
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
01:15Diabetes: Symptoms, Diagnosis, and Complications
01:20Pathophysiology of Diabetes
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
01:10Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...