Easy and Low-Cost Identification of Metabolic Syndrome in Patients Treated With Second-Generation Antipsychotics: Artificial Neural Network and Logistic Regression Models
J Clin Psychiatry 2010;71(3):225-234
© Copyright 2016 Physicians Postgraduate Press, Inc.
Purchase This PDF for $40.00
If you are not a paid subscriber, you may purchase the PDF.
(You'll need the free Adobe Acrobat Reader.)
Receive immediate full-text access to JCP. You can subscribe to JCP online-only ($86) or print + online ($156 individual).
With your subscription, receive a free PDF collection of the NCDEU Festschrift articles. Hurry! This offer ends December 31, 2011.
If you are a paid subscriber to JCP and do not yet have a username and password, activate your subscription now.
As a paid subscriber who has activated your subscription, you have access to the HTML and PDF versions of this item.
Click here to login.
Did you forget your password?
Still can't log in? Contact the Circulation Department at 1-800-489-1001 x4 or send email
Objective: Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment.
Method: A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder
(DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data
for these patients were collected between March 2005 and September 2005. The input variables
of ANN and logistic regression were limited to
demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models.
Results: Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean ± SD AUC was high for both the ANN and logistic regression models (0.934 ± 0.033 vs 0.922 ± 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models.
Conclusion: Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients.
Submitted: August 13, 2008; accepted November 26, 2008.
Online ahead of print: October 6, 2009.
Corresponding authors: Hung-Wen Chiu, PhD, 250, Wusing St, Sinyi District, Taipei 110, Taiwan (email@example.com), and Yu-Chuan Li, MD, PhD, 250, Wunsing St, Sinyi District, Taipei 110, Taiwan