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Predictive Models of Metabolic Syndrome for Patients with Schizophrenic or Bipolar Disorders

Published online by Cambridge University Press:  16 April 2020

P. Garcia-Portilla ,
S. Santamarina ,
P.A. Saiz ,
F. Sánchez ,
E. Diaz-Mesa ,
C. Iglesias  and
J. Bobes
P. Garcia-Portilla
Affiliation:
Psychiatry, University of Oviedo, Oviedo, Spain CIBERSAM, University of Oviedo, Oviedo, Spain
S. Santamarina
Affiliation:
Mental Health Services, SESPA, Langreso, Oviedo, Spain
P.A. Saiz
Affiliation:
Psychiatry, University of Oviedo, Oviedo, Spain CIBERSAM, University of Oviedo, Oviedo, Spain
F. Sánchez
Affiliation:
Tecniproject, Avilés, Oviedo, Spain
E. Diaz-Mesa
Affiliation:
CIBERSAM, University of Oviedo, Oviedo, Spain
C. Iglesias
Affiliation:
Mental Health Services, SESPA, Oviedo, Spain
J. Bobes
Affiliation:
Psychiatry, University of Oviedo, Oviedo, Spain CIBERSAM, University of Oviedo, Oviedo, Spain

Abstract

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Introduction

Metabolic syndrome is a frequent, severe, undiagnosed physical comorbidity in patients with severe mental disorders.

Aim

To develop a predictive model of metabolic syndrome for patients with schizophrenic or bipolar disorders, useful for both clinical practice and research.

Methods

Naturalistic, one-year follow-up study conducted in Asturias, Spain. A total of 172 patients with schizophrenic (Sch-P) or bipolar (BD-P) disorders (ICD-10 criteria), under maintenance treatment, who gave written informed consent were included. Metabolic syndrome was defined according to the modified NCEP ATP-III criteria. Multivariate Adaptive Regression Splines (MARS), Genetic Algorithms (GA), and Support Vector Machine (SVM) analysis were performed.

Results

Starting from a large set of demographic and clinical variables, and by means of intermediate MARS and GA models, an SVM model able to classify if a patient with schizophrenia or bipolar disorder suffers from metabolic syndrome with an accuracy of 98.68% (sensitivity 100%, specifity 94.4%) was obtained. The final model only needs 6 variables: Sch-P:

  1. (1) Low HDL-cholesterol,

  2. (2) Fasting glucose level,

  3. (3) Family history of obesity,

  4. (4) Triglyceride level,

  5. (5) Family history of dyslipidemia, and

  6. (6) Use of antidepressants; BD-P: (1), (2), (3),

  7. (7) Use of lipid-lowering medication,

  8. (8) Use of antipsychotics, and

  9. (9) Use of mood stabilizers.

Conclusion

We developed a simple and easy to use predictive model to identify metabolic syndrome in patients with schizophrenic or bipolar disorders.

Type
P03-222
Information
European Psychiatry , Volume 26 , Issue S2: Abstracts of the 19th European Congress of Psychiatry , March 2011 , pp. 1391
Copyright
Copyright © European Psychiatric Association 2011
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