Related Experiment Video
Updated: Jan 30, 2026
A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
Published on: July 5, 2022
Development and validation of algorithms to identify newly diagnosed type 1 and type 2 diabetes in pediatric
Dana Y Teltsch1, Soulmaz Fazeli Farsani2, Richard S Swain1,3
1Real-world Evidence, Evidera, Waltham, MA, USA.
Insights
Algorithms using claims data accurately identify type 1 (T1DM) and type 2 (T2DM) diabetes in pediatric patients. This facilitates large-scale studies on childhood diabetes.
Area of Science:
- Pediatric Endocrinology
- Health Informatics
- Diabetes Research
Background:
- Accurate classification of diabetes type in pediatric patients is crucial for appropriate management and research.
- Distinguishing between type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in children can be challenging.
- Existing methods for diabetes classification may not be readily applicable to large administrative datasets.
Purpose of the Study:
- To develop and validate algorithms for classifying diabetes type in newly diagnosed pediatric patients.
- To assess the accuracy of these algorithms using key performance metrics.
- To enable large-scale research on pediatric T1DM and T2DM.
Main Methods:
- Utilized data from the US Department of Defense health system for patients aged 10-18 years with incident diabetes mellitus (DM).
- Developed and validated algorithms using two independent sets of 200 children, employing clinical insight, literature, and quantitative approaches.
- Assessed algorithm performance using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) based on chart review.
Main Results:
- The most effective algorithms were derived from claims data, incorporating factors like glucose-lowering drug use, DM diagnosis codes, and comorbidities.
- The best-performing algorithms achieved high accuracy: T2DM (90% sensitivity, 95% specificity, 87% PPV, 96% NPV) and T1DM (98% sensitivity, 95% specificity, 98% PPV, 96% NPV).
Conclusions:
- Claims-based algorithms demonstrate high accuracy in identifying newly diagnosed T1DM and T2DM in pediatric populations.
- These algorithms can significantly aid in conducting large database studies involving children with T1DM and T2DM.
- External validation of these algorithms in diverse data sources is recommended for broader applicability.
Purpose:
To develop and validate algorithms to classify diabetes type in newly diagnosed pediatric patients with DM.
Method:
Data from the United States Department of Defense health system were used to identify patients aged 10 to 18 years with incident DM. Two independent sets of 200 children were randomly sampled for algorithm development and validation. Algorithms were developed based on clinical insight, published literature, and quantitative approaches. The actual DM type was ascertained via chart review. Finally, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated.
Results:
Among the 400 patients, mean age was 14.2 (±2.5 years), and 50% were female. The best performing algorithms were based on data available in claims. They consisted of several logical expressions based on one predictor or more, which classified patients by use of glucose-lowering drugs or testing, DM ICD-9 diagnosis codes, and comorbidities. The best performing T2DM and T1DM algorithms achieved 90% and 98% sensitivity, 95% and 95% specificity, 87% and 98% PPV, and 96% and 96% NPV, respectively.
Conclusions:
Our results suggest that claims algorithms can accurately identify newly diagnosed T1DM and T2DM pediatric patients, which can facilitate large database studies in children with T1DM and T2DM. However, external validation in other data sources is needed.
More Related Videos
Related Concept Videos
01:22Diabetes Mellitus: Type 2 and Gestational
01:19Data: Types and Distribution
Distributions in...
01:11Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
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...
01:24Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:27Types of Records I: Unit and Nurses Records
Unit records can be divided into two main types: administrative records and clinical records.
Administrative records in...