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)

Farooq, Saeed; Hattle, Miriam; Kingstone, Tom; Ajnakina, Olesya; Dazzan, Paola; Demjaha, Arsime; Murray, Robin M.; Di Forti, Marta; Jones, Peter B; Doody, Gillian A.; Shiers, David; Andrews, Gabrielle; Milner, Abbie; Nettis, Maria Antonietta; Lawrence, Andrew J.; van der Windt, Danielle A.; Riley, Richard D.

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.

Citation

Farooq, S., Hattle, M., Kingstone, T., Ajnakina, O., Dazzan, P., Demjaha, A., …Riley, R. D. (2024). 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). British Journal of Psychiatry, 1-10. https://doi.org/10.1192/bjp.2024.101

• Journal Article Type: Article
• Acceptance Date: May 1, 2024
• Online Publication Date: Aug 5, 2024
• Publication Date: 2024-09
• Deposit Date: Aug 22, 2024
• Journal: The British Journal of Psychiatry
• Print ISSN: 0007-1250
• Electronic ISSN: 1472-1465
• Publisher: Royal College of Psychiatrists
• Peer Reviewed: Peer Reviewed
• Pages: 1-10
• DOI: https://doi.org/10.1192/bjp.2024.101
• Keywords: prognostic model, mixed methods, First-episode schizophrenia, treatment resistant, decision analysis
• Public URL: https://keele-repository.worktribe.com/output/887310
• Publisher URL: https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/development-and-initial-evaluation-of-a-clinical-prediction-model-for-risk-of-treatment-resistance-in-firstepisode-psychosis-schizophrenia-prediction-of-resistance-to-treatm
• Related Public URLs: AAM - https://kclpure.kcl.ac.uk/portal/en/publications/development-and-initial-evaluation-of-a-clinical-prediction-model
• PMID: 39101211
• Additional Information: Copyright: Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Royal College of Psychiatrists