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Prediction of quality of life in schizophrenia using machine learning models on data from clinical antipsychotic trials of intervention effectiveness (CATIE) schizophrenia trial

Published online by Cambridge University Press:  13 August 2021

M. Beaudoin ,
S. Potvin ,
A. Hudon ,
C.-E. Giguère  and
A. Dumais
M. Beaudoin*
Affiliation:
Psychiatry And Addictology, University of Montreal, Montreal, Canada Centre De Recherche, Institut universitaire en santé mentale de Montréal, Montréal, Canada Faculty Of Medicine, McGill University, Montreal, Canada
S. Potvin
Affiliation:
Psychiatry And Addictology, University of Montreal, Montreal, Canada Centre De Recherche, Institut universitaire en santé mentale de Montréal, Montréal, Canada
A. Hudon
Affiliation:
Psychiatry And Addictology, University of Montreal, Montreal, Canada Centre De Recherche, Institut universitaire en santé mentale de Montréal, Montréal, Canada
C.-E. Giguère
Affiliation:
Centre De Recherche, Institut universitaire en santé mentale de Montréal, Montréal, Canada
A. Dumais
Affiliation:
Psychiatry And Addictology, University of Montreal, Montreal, Canada Centre De Recherche, Institut universitaire en santé mentale de Montréal, Montréal, Canada Psychiatry, Institut national de psychiatrie légale Philippe-Pinel, Montreal, Canada
*
*Corresponding author.

Abstract

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Introduction

Schizophrenia is a chronic and severe mental disorder. While research focus remains mainly on negative outcomes, it is questionable whether we are placing enough emphasis on improving their sense of well-being and functioning. This could be accessed through the study of the quality of life (QoL). To date, QoL prediction models mainly focused on neurocognition and psychotic symptoms, but their predictive power remained limited.

Objectives

The aim is to accurately predict the QoL within schizophrenia using unsupervised learning methods.

Methods

We computed variables from 952 patients from the CATIE study, a randomized, double-blind clinical trial for schizophrenia treatment. QoL was measured using the Heinrichs-Carpenter Quality of Life Scale and potential predictors included almost all available variables: symptoms, neurocognition, medication adherence, insight, adverse effects, etc. By optimizing parameters to reach optimal models, three linear regressions were calculated: (1) baseline predictors of 12-month QoL, (2) 6-month predictors of 12-month QoL, and (3) baseline predictors of 6-month QoL. Adjustments were made to ensure that included variables were not collinear nor redundant with QoL.

Results

Calculated models had adjusted R-squared of 0.918, 0.922 and 0.913, respectively. Best predictors were medication side effects, sociodemographic and neurocognitive variables. Low psychotic and depressive symptoms were also included, as well as lab values suggesting the absence of problems with chloremia and calcemia.

Conclusions

Calculated predictive models explain almost all subsequent QoL. It appears that physical health variables, generally omitted from mental health-related studies, have an important impact on patients’ QoL. Therefore, interventions should also consider these aspects.

Disclosure

No significant relationships.

Keywords

schizophréniaquality of lifemachine learningpredictive models
Type
Abstract
Information
European Psychiatry , Volume 64 , Special Issue S1: Abstracts of the 29th European Congress of Psychiatry , April 2021 , pp. S157
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the European Psychiatric Association
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