Prediction of quality of life in schizophrenia using machine learning models on data from Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial

Mélissa Beaudoin, Alexandre Hudon, Charles-Edouard Giguère, Stéphane Potvin, Alexandre Dumais, Mélissa Beaudoin, Alexandre Hudon, Charles-Edouard Giguère, Stéphane Potvin, Alexandre Dumais

Abstract

While research focus remains mainly on psychotic symptoms, it is questionable whether we are placing enough emphasis on improving the quality of life (QoL) of schizophrenia patients. To date, the predictive power of QoL remained limited. Therefore, this study aimed to accurately predict the QoL within schizophrenia using supervised learning methods. The authors report findings from participants of a large randomized, double-blind clinical trial for schizophrenia treatment. Potential predictors of QoL included all available and non-redundant variables from the dataset. By optimizing parameters, three linear LASSO regressions were calculated (N = 697, 692, and 786), including 44, 47, and 41 variables, with adjusted R-squares ranging from 0.31 to 0.36. Best predictors included social and emotion-related symptoms, neurocognition (processing speed), education, female gender, treatment attitudes, and mental, emotional, and physical health. These results demonstrate that machine learning is an excellent predictive tool to process clinical data. It appears that the patient's perception of their treatment has an important impact on patients' QoL and that interventions should consider this aspect.Trial registration: ClinicalTrials.gov Identifier: NCT00014001.

Conflict of interest statement

The authors declare no direct competing interests. A.D. and S.P. received grants from Otsuka Pharmaceuticals and from HLS Therapeutics, but these were not related to the present research project. A.D. is funded by the Fonds de recherche du Québec—Santé (FRQS), and M.B. is funded by the Canadian Institutes of Health Research (CIHR).

© 2022. The Author(s).

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