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Development and initial evaluation of a clinical prediction model for risk of treatment resistance in first-episode psychosis: Schizophrenia Prediction of Resistance to Treatment (SPIRIT)

Published online by Cambridge University Press:  05 August 2024

Saeed Farooq Open the ORCID record for Saeed Farooq [Opens in a new window] ,
Miriam Hattle ,
Tom Kingstone ,
Olesya Ajnakina ,
Paola Dazzan ,
Arsime Demjaha Open the ORCID record for Arsime Demjaha [Opens in a new window] ,
Robin M. Murray ,
Marta Di Forti Open the ORCID record for Marta Di Forti [Opens in a new window] ,
Peter B. Jones Open the ORCID record for Peter B. Jones [Opens in a new window]  and
Gillian A. Doody
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Saeed Farooq*
Affiliation:
School of Medicine, Keele University, Newcastle-under-Lyme, UK; National Institute for Health and Care Research (NIHR), UK; and St George's Hospital, Midlands Partnership University NHS Foundation Trust, Stafford, UK
Miriam Hattle
Affiliation:
Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
Tom Kingstone
Affiliation:
School of Medicine, Keele University, Newcastle-under-Lyme, UK; National Institute for Health and Care Research (NIHR), UK; and St George's Hospital, Midlands Partnership University NHS Foundation Trust, Stafford, UK
Olesya Ajnakina
Affiliation:
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; and Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK
Paola Dazzan
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Arsime Demjaha
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Robin M. Murray
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; and Department of Psychiatry, Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Palermo, Italy
Marta Di Forti
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Peter B. Jones
Affiliation:
Department of Psychiatry, University of Cambridge, Cambridge, UK
Gillian A. Doody
Affiliation:
Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, UK
David Shiers
Affiliation:
School of Medicine, Keele University, Newcastle-under-Lyme, UK; Psychosis Research Unit, Greater Manchester Mental Health NHS Trust, Manchester, UK; and University of Manchester, Manchester, UK
Gabrielle Andrews
Affiliation:
St George's Hospital, Midlands Partnership University NHS Foundation Trust, Stafford, UK
Abbie Milner
Affiliation:
Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
Maria Antonietta Nettis
Affiliation:
South London and Maudsley NHS Foundation Trust, London, UK; and Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Andrew J. Lawrence
Affiliation:
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Danielle A. van der Windt
Affiliation:
Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK
Richard D. Riley
Affiliation:
Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; and National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
*
Correspondence: Saeed Farooq. Email: s.farooq@keele.ac.uk
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Abstract

Background

A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.

Aims

To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.

Method

We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model.

Results

We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.

Conclusions

We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.

Keywords

First-episode schizophreniatreatment resistantprognostic modeldecision analysismixed methods
Type
Original Article
Information
The British Journal of Psychiatry , First View , pp. 1 - 10
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Royal College of Psychiatrists

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