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EPA-0882 - Prediction of Diagnosis of Early-Onset Schizophrenia Spectrum Disorders Using Support Vector Machines

Published online by Cambridge University Press:  15 April 2020

L. Pina-Camacho
Affiliation:
Child and Adolescent Psychiatry Department, CIBERSAM. Instituto de Investigación Sanitaria Gregorio Marañon IiSGM. Hospital General Universitario Gregorio Marañón, Madrid, Spain
C.M. Diaz-Caneja
Affiliation:
Child and Adolescent Psychiatry Department, CIBERSAM. Instituto de Investigación Sanitaria Gregorio Marañon IiSGM. Hospital General Universitario Gregorio Marañón, Madrid, Spain
J. Garcia-Prieto
Affiliation:
Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology (CTB). Universidad Politécnica de Madrid, Madrid, Spain
M. Parellada
Affiliation:
Child and Adolescent Psychiatry Department, CIBERSAM. Instituto de Investigación Sanitaria Gregorio Marañon IiSGM. Hospital General Universitario Gregorio Marañón, Madrid, Spain
J. Castro-Fornieles
Affiliation:
Child and Adolescent Psychiatry and Psychology Department, Neuroscience Institute Hospital Clínic Barcelona IDIBAPS SGR-1119 University of Barcelona. CIBERSAM, Barcelona, Spain
A. Gonzalez-Pinto
Affiliation:
International Mood Disorders Research Centre, Hospital Santiago Apóstol. University of the Basque Country, Vitoria, Spain
I. Bombin
Affiliation:
Reintegra, Neuro-Rehabilitation Center, Oviedo, Spain
M. Graell
Affiliation:
Section of Child and Adolescent Psychiatry and Psychology, CIBERSAM. Hospital Infantil Universitario Niño Jesús, Madrid, Spain
S. Otero
Affiliation:
Child and Adolescent Mental Health Unit Department of Psychiatry and Psychology, CIBERSAM Hospital Universitario Marqués de Valdecilla, Santander, Spain
M. Rapado-Castro
Affiliation:
Child and Adolescent Psychiatry Department, CIBERSAM. Instituto de Investigación Sanitaria Gregorio Marañon IiSGM. Hospital General Universitario Gregorio Marañón, Madrid, Spain
J. Janssen
Affiliation:
Child and Adolescent Psychiatry Department, CIBERSAM. Instituto de Investigación Sanitaria Gregorio Marañon IiSGM. Hospital General Universitario Gregorio Marañón, Madrid, Spain
I. Baeza
Affiliation:
Child and Adolescent Psychiatry and Psychology Department, Neuroscience Institute Hospital Clínic Barcelona IDIBAPS SGR-1119 University of Barcelona. CIBERSAM, Barcelona, Spain
F. Del Pozo
Affiliation:
Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology (CTB). Universidad Politécnica de Madrid, Madrid, Spain
M. Desco
Affiliation:
Department of Experimental Medicine, CIBERSAM Instituto de Investigación Sanitaria Gregorio Marañon IiSGM Hospital General Universitario Gregorio Marañón, Madrid, Spain
C. Arango
Affiliation:
Child and Adolescent Psychiatry Department, CIBERSAM. Instituto de Investigación Sanitaria Gregorio Marañon IiSGM. Hospital General Universitario Gregorio Marañón, Madrid, Spain
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Abstract

Diagnosis of schizophrenia spectrum disorders (SSD) may be difficult in clinical practice, particularly during the first episodes of early-onset psychosis (FE-EOP).

Aims:

To develop a Support Vector Machine (SVM) algorithm as a predictive tool for diagnostic outcome in patients with FE-EOP, based on clinical and biomedical data at the emergence of the illness.

Methods:

Two-year, prospective longitudinal study, where 81 patients (9-17 years of age) with a FE-EOP and stable diagnosis at follow-up and 41 age and sex-matched healthy controls (HC) were included. Structured diagnostic interviews, clinical and cognitive scales, a MRI scan and biochemical tests were conducted at baseline. Three SVM classification algorithms were developed (SSD vs HC group, non-SSD vs HC group, and SSD vs non-SSD group). Jackknifing was used to validate the algorithms and to calculate performance estimates. Enhanced-Recursive Feature Elimination was performed in order to gain information about the predictive weight for diagnosis of each variable.

Results:

The SSD-versus-non-SSD classifier achieved an overall accuracy of 83.1%, sensitivity of 86.6% and specificity of 77.8%. The variables during a FE-EOP with higher predictive value for a diagnosis of SSD were clinical variables such as negative symptoms preceding or during the psychotic onset, poor insight and duration of illness until first psychiatric contact. Biochemical, neuroimaging, and cognitive variables at baseline did not provide any additional predictive value.

Conclusions:

SVM may serve as a predictive tool for early diagnosis of SSD during a FE-EOP. The most discriminative variables during a FE-EOP for a future diagnosis of SSD are clinical variables.

Type
FC04 - Free Communications Session 04: Child and Adolescent Psychiatry
Copyright
Copyright © European Psychiatric Association 2014

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