title: A simple prediction rule and a neural network model to predict pancreatic beta-cell reserve in young adults with diabetes mellitus.
abstract: In the present study we developed and assessed the performance of a simple prediction rule and a neural network model to predict beta-cell reserve in young adults with diabetes. Eighty three young adults with diabetes were included in the study. All were less than 40 years old and without apparent secondary causes of diabetes. The subjects were randomly allocated to 2 groups; group 1 (n = 59) for developing a prediction rule and training a neural network, group 2 (n = 24) for validation purpose. The prediction rule was developed by using stepwise logistic regression. Using stepwise logistic regression and modification of the derived equation, the patient would be insulin deficient if 3(waist circumference in cm) + 4(age at diagnosis) < 340 in the absence of previous diabetic ketoacidosis (DKA) or < 400 in the presence of previous DKA. When tested in the validation set, the prediction rule had positive and negative predictive values of 86.7 per cent and 77.8 per cent respectively with 83.3 per cent accuracy while the ANN model had a positive predictive value of 88.2 per cent and a negative predictive value of 100 per cent with 91.7 per cent accuracy. When testing the performance of the prediction rule and the ANN model compared to the assessment of 23 internists in a subgroup of 9 diabetics whose age at onset was less than 30 years and without a history of DKA, the ANN had the highest ability to predict beta-cell reserve (accuracy = 88.9), followed by the prediction rule (accuracy = 77.8%) and assessments by internists (accuracy = 60.9%). We concluded that beta-cell reserve in young adults with diabetes mellitus could be predicted by a simple prediction rule or a neural network model. The prediction rule and the neural network model can be helpful clinically in patients with mixed clinical features of type 1 and type 2 diabetes.
authors.affiliation: Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
authors.forename: S
authors.initials: S
authors.lastname: Thamprajamchit
authors.forename: B
authors.initials: B
authors.lastname: Ongphiphadhanakul
authors.forename: S
authors.initials: S
authors.lastname: Krittiyawong
authors.forename: S
authors.initials: S
authors.lastname: Chanprasertyothin
authors.forename: P
authors.initials: P
authors.lastname: Bunnag
authors.forename: R
authors.initials: R
authors.lastname: Rajatanavin
authors.forename: G
authors.initials: G
authors.lastname: Puavilai
mesh_terms: D000293:Adolescent; D000328:Adult; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007515:Islets of Langerhans; D016015:Logistic Models; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests