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Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus
Author(s) -
PinhasHamiel Orit,
Hamiel Uri,
Greenfield Yuval,
Boyko Valentina,
GraphBarel Chana,
Rachmiel Marianna,
LernerGeva Liat,
Reichman Brian
Publication year - 2013
Publication title -
international journal of eating disorders
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.785
H-Index - 138
eISSN - 1098-108X
pISSN - 0276-3478
DOI - 10.1002/eat.22138
Subject(s) - percentile , insulin , diabetes mellitus , type 2 diabetes mellitus , type 2 diabetes , medicine , decision tree , machine learning , endocrinology , computer science , statistics , mathematics
Objective 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. Method 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. Results 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. Discussion 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)