The relationship between cognitive deficits and impaired short-term functional outcome in clinical high-risk for psychosis participants: a machine learning and modelling approach

Haining, K., Brunner, G., Gajwani, R. , Gross, J. , Gumley, A. I. , Lawrie, S. M., Schwannauer, M., Schultze-Lutter, F. and Uhlhaas, P. J. (2021) The relationship between cognitive deficits and impaired short-term functional outcome in clinical high-risk for psychosis participants: a machine learning and modelling approach. Schizophrenia Research, 231, pp. 24-31. (doi: 10.1016/j.schres.2021.02.019) (PMID:33744682)

Abstract

Poor functional outcomes are common in individuals at clinical high-risk for psychosis (CHR-P), but the contribution of cognitive deficits remains unclear. We examined the potential utility of cognitive variables in predictive models of functioning at baseline and follow-up with machine learning methods. Additional models fitted on baseline functioning variables were used as a benchmark to evaluate model performance. Data were available for 1) 146 CHR-P individuals of whom 118 completed a 6- and/or 12-month follow-up, 2) 47 participants not fulfilling CHR criteria (CHR-Ns) but displaying affective and substance use disorders and 3) 55 healthy controls (HCs). Predictors of baseline global assessment of functioning (GAF) scores were selected by L1-regularised least angle regression and then used to train classifiers to predict functional outcome in CHR-P individuals. In CHR-P participants, cognitive deficits together with clinical and functioning variables explained 41% of the variance in baseline GAF scores while cognitive variables alone explained 12%. These variables allowed classification of functional outcome with an average balanced accuracy (BAC) of 63% in both mixed- and cross-site models. However, higher accuracies (68%–70%) were achieved using classifiers fitted only on baseline functioning variables. Our findings suggest that cognitive deficits, alongside clinical and functioning variables, displayed robust relationships with impaired functioning in CHR-P participants at baseline and follow-up. Moreover, these variables allow for prediction of functional outcome. However, models based on baseline functioning variables showed a similar performance, highlighting the need to develop more accurate algorithms for predicting functional outcome in CHR-P participants.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Uhlhaas, Professor Peter and Brunner, Gina and Haining, Dr Kate and Gajwani, Dr Ruchika and Gross, Professor Joachim and Gumley, Professor Andrew
• Authors: Haining, K., Brunner, G., Gajwani, R., Gross, J., Gumley, A. I., Lawrie, S. M., Schwannauer, M., Schultze-Lutter, F., and Uhlhaas, P. J.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Mental Health and Wellbeing; College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Schizophrenia Research
• Publisher: Elsevier
• ISSN: 0920-9964
• ISSN (Online): 1573-2509
• Published Online: 18 March 2021
• Copyright Holders: Copyright © 2021 Elsevier B.V.
• First Published: First published in Schizophrenia Research 231:24-31
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
190713		Using Magnetoencephalography to Investigate Aberrant Neural Synchrony in Prodromal Schizophrenia: A Translational Biomarker Approach	Peter Uhlhaas	Medical Research Council (MRC)	MR/L011689/1	NP - Centre for Cognitive Neuroimaging (CCNi)