Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus
Article first published online: 15 MAY 2013
Copyright © 2013 Wiley Periodicals, Inc.
International Journal of Eating Disorders
Volume 46, Issue 8, pages 819–825, December 2013
How to Cite
Pinhas-Hamiel, O., Hamiel, U., Greenfield, Y., Boyko, V., Graph-Barel, C., Rachmiel, M., Lerner-Geva, L. and Reichman, B. (2013), Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus. Int. J. Eat. Disord., 46: 819–825. doi: 10.1002/eat.22138
- Issue published online: 20 NOV 2013
- Article first published online: 15 MAY 2013
- Manuscript Accepted: 25 MAR 2013
- Manuscript Revised: 24 MAR 2013
- Insulin omission;
- Hemoglobin A1c;
- data mining;
- type 1 diabetes;
- decision tree
Intentional insulin omission is a unique inappropriate compensatory behavior that occurs in patients with type 1 diabetes mellitus, mostly in females, who omit or restrict their required insulin doses in order to lose weight. Diagnosis of this underlying disorder is difficult. We aimed to use clinical and laboratory criteria to create an algorithm to assist in the detection of intentional insulin omission.
The distribution of HbA1c levels from 287 (181 females) patients with type 1 diabetes were used as reference. Data from 26 patients with type 1 diabetes and intentional insulin omission were analysed. The Weka (Waikato Environment for Knowledge Analysis) machine learning software, decision tree classifier with 10-fold cross validation was used to developed prediction models. Model performance was assessed by cross-validation in a further 43 patients.
Adolescents with intentional insulin omission were discriminated by: female sex, HbA1c>9.2%, more than 20% of HbA1c measurements above the 90th percentile, the mean of 3 highest delta HbA1c z-scores>1.28, current age and age at diagnosis. The models developed showed good discrimination (sensitivity and specificity 0.88 and 0.74, respectively). The external test dataset revealed good performance of the model with a sensitivity and specificity of 1.00 and 0.97, respectively.
Using data mining methods we developed a clinical prediction model to determine an individual's probability of intentionally omitting insulin. This model provides a decision support system for the detection of intentional insulin omission for weight loss in adolescent females with type 1 diabetes mellitus. © 2013 Wiley Periodicals, Inc. (Int J Eat Disord 2013; 46:819–825)