Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model.

FEP Modelling Prediction Schizophrenia Treatment resistance

Journal

Schizophrenia research
ISSN: 1573-2509
Titre abrégé: Schizophr Res
Pays: Netherlands
ID NLM: 8804207

Informations de publication

Date de publication:
10 Sep 2024
Historique:
received: 07 06 2024
revised: 07 08 2024
accepted: 06 09 2024
medline: 12 9 2024
pubmed: 12 9 2024
entrez: 11 9 2024
Statut: aheadofprint

Résumé

Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP). Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures. The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56). Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.

Sections du résumé

BACKGROUND BACKGROUND
Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP).
METHODS METHODS
Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures.
RESULTS RESULTS
The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56).
CONCLUSIONS CONCLUSIONS
Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.

Identifiants

DOI: 10.1016/j.schres.2024.09.010 PMID: 39260340
pubmed: 39260340
pii: S0920-9964(24)00421-3
doi: 10.1016/j.schres.2024.09.010
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

66-77

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest None.

Auteurs

Rebecca Lee (R)

Institute for Mental Health, University of Birmingham, UK; Centre for Youth Mental Health, University of Melbourne, Australia. Electronic address: RXL723@student.bham.ac.uk.

Sian Lowri Griffiths (SL)

Institute for Mental Health, University of Birmingham, UK.

Georgios V Gkoutos (GV)

Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, UK; Health Data Research UK, Midlands Site, Birmingham, UK.

Stephen J Wood (SJ)

Centre for Youth Mental Health, University of Melbourne, Australia; Orygen, Melbourne, Australia; School of Psychology, University of Birmingham, UK.

Laura Bravo-Merodio (L)

Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, University of Birmingham, UK.

Paris A Lalousis (PA)

Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK.

Linda Everard (L)

Birmingham and Solihull Mental Health Foundation Trust, Birmingham, UK.

Peter B Jones (PB)

Department of Psychiatry, University of Cambridge and CAMEO, Cambridge and Peterborough NHS Foundation Trust, Fulbourn, UK.

David Fowler (D)

Department of Psychology, University of Sussex, Brighton, UK.

Joanne Hodegkins (J)

Norwich Medical School, University of East Anglia, Norwich, UK.

Tim Amos (T)

Academic Unit of Psychiatry, University of Bristol, Bristol, UK.

Nick Freemantle (N)

Institute of Clinical Trials and Methodology, University College London, London, UK.

Swaran P Singh (SP)

Coventry and Warwickshire Partnership NHS Trust, UK; Mental Health and Wellbeing Warwick Medical School, University of Warwick, Coventry, UK.

Max Birchwood (M)

Mental Health and Wellbeing Warwick Medical School, University of Warwick, Coventry, UK.

Rachel Upthegrove (R)

Institute for Mental Health, University of Birmingham, UK; Birmingham Early Intervention Service, Birmingham Women's and Children's NHS Foundation Trust, UK.

Classifications MeSH