Skip to main content

Research Repository

Advanced Search

Study protocol for the development and internal validation of SPIRIT (Schizophrenia Prediction of Resistance to Treatment): A clinical tool for predicting risk of treatment resistance to anti-psychotics in First Episode Schizophrenia

Farooq, Saeed; Hattle, Miriam; Dazzan, Paola; Kingstone, Tom; Ajnakina, Olesya; Shiers, David; Nettis, Maria Antonietta; Lawrence, Andrew; Riley, Richard D.; Van Der Windt, Danielle

Study protocol for the development and internal validation of SPIRIT (Schizophrenia Prediction of Resistance to Treatment): A clinical tool for predicting risk of treatment resistance to anti-psychotics in First Episode Schizophrenia Thumbnail


Authors

Miriam Hattle

Paola Dazzan

Olesya Ajnakina

David Shiers

Maria Antonietta Nettis

Andrew Lawrence

Richard D. Riley



Abstract

<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>Treatment Resistant Schizophrenia (TRS) is associated with significant impairment of functioning and high treatment costs. Identification of patients at high risk of TRS at their initial diagnosis may significantly improve clinical outcomes and minimize social and functional disability. We aim to develop a prognostic model for predicting the risk of TRS in patients with First Episode Schizophrenia, and to examine its potential utility and acceptability as a clinical decision tool.</jats:p></jats:sec><jats:sec><jats:title>Methods and analysis</jats:title><jats:p>We will use two well-characterised UK-based first episode psychosis cohorts: AESOP-10 and GAP for which data has been collected on sociodemographic and clinical characteristics. We will identify candidate predictors for the model based on current literature and stakeholder consultation. Model development will use all data, with the number of candidate predictors restricted according to available sample size and event rate. A model for predicting risk of TRS will be developed based on penalised regression, with missing data handled using multiple imputation. Internal validation will be undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model’s performance. The clinical utility of the model in terms of clinically relevant risk thresholds will be evaluated using net benefit and decision curves (comparative to competing strategies). Consultation with patients and clinical stakeholders will determine potential thresholds of risk for treatment decision making. The acceptability of embedding the model as a clinical tool will be explored using focus groups with clinicians in early intervention services.</jats:p></jats:sec><jats:sec><jats:title>Ethics and dissemination</jats:title><jats:p>The development of the prognostic model will be based on anonymised data from existing cohorts, for which ethical approval is in place. Ethical approval has been obtained from Keele University for the qualitative focus groups within Early Intervention in Psychosis services (Ref: MH-210174). Findings will be shared through peer-review publications, conference presentations and social media. A lay summary will be published on collaborator websites.</jats:p></jats:sec><jats:sec><jats:title>Strengths and limitations of this study</jats:title><jats:list list-type="bullet"><jats:list-item><jats:p>The proposed study is the first step on the road towards the design and evaluation of a prognostic model and decision tool for the identification of treatment resistant schizophrenia. This could be informative to clinicians, patients, and their care providers in shared decision making and improvement of treatment plans.</jats:p></jats:list-item><jats:list-item><jats:p>Individual participant data from two existing cohorts will be used to develop and internally validate the prognostic model.</jats:p></jats:list-item><jats:list-item><jats:p>Using a mixed method design improves the ability to understand the limitations of the tool in a clinical context and create a foundation to develop it to be more effective.</jats:p></jats:list-item><jats:list-item><jats:p>A limitation of the development of this tool is that the number of people with TRS may not be sufficiently large to consider all potential predictors for the model</jats:p></jats:list-item><jats:list-item><jats:p>Further testing of the external validity of the prognostic model will be required</jats:p></jats:list-item></jats:list></jats:sec>

Journal Article Type Article
Acceptance Date Mar 16, 2022
Online Publication Date Apr 8, 2022
Publicly Available Date May 30, 2023
Journal BMJ Open
Electronic ISSN 2044-6055
Publisher BMJ Publishing Group
Peer Reviewed Peer Reviewed
Volume 12
Issue 4
DOI https://doi.org/10.1136/bmjopen-2021-056420
Publisher URL https://www.medrxiv.org/content/10.1101/2022.02.15.22270460v1