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首发精神分裂症治疗抵抗预测(SPIRIT)研究方案:一种用于预测首发精神分裂症患者抗精神病药物治疗抵抗风险的临床工具的开发和内部验证。

Study protocol for the development and internal validation of Schizophrenia Prediction of Resistance to Treatment (SPIRIT): a clinical tool for predicting risk of treatment resistance to antipsychotics in first-episode schizophrenia.

机构信息

Midlands Partnership NHS Foundation Trust, Stafford, Staffordshire, UK

School of Medicine, Keele University, Keele, Staffordshire, UK.

出版信息

BMJ Open. 2022 Apr 8;12(4):e056420. doi: 10.1136/bmjopen-2021-056420.

Abstract

INTRODUCTION

Treatment-resistant schizophrenia (TRS) is associated with significant impairment of functioning and high treatment costs. Identification of patients at high risk of TRS at the time of their initial diagnosis may significantly improve clinical outcomes and minimise social and functional disability. We aim to develop a prognostic model for predicting the risk of developing TRS in patients with first-episode schizophrenia and to examine its potential utility and acceptability as a clinical decision tool.

METHODS AND ANALYSIS

We will use two well-characterised longitudinal UK-based first-episode psychosis cohorts: Aetiology and Ethnicity in Schizophrenia and Other Psychoses and Genetics and Psychosis for which data have 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 qualitative focus groups with up to 20 clinicians in total from early intervention services. Clinicians will be recruited from services in Stafford and London with the focus groups being held via an online platform.

ETHICS AND DISSEMINATION

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). Suitable processes are in place to obtain informed consent for National Health Service staff taking part in interviews or focus groups. A study information sheet with cover letter and consent form have been prepared and approved by the local Research Ethics Committee. Findings will be shared through peer-reviewed publications, conference presentations and social media. A lay summary will be published on collaborator websites.

摘要

简介

治疗抵抗性精神分裂症(TRS)与功能显著受损和高治疗费用相关。在初次诊断时识别出有较高 TRS 风险的患者,可能会显著改善临床结局,并最大限度减少社会和功能残疾。我们旨在为首发精神分裂症患者开发一种预测发生 TRS 风险的预后模型,并检验其作为临床决策工具的潜在效用和可接受性。

方法和分析

我们将使用两个具有良好特征的英国首发精神病队列:精神分裂症及其他精神病的病因和种族和精神分裂症及其他精神病的遗传学,其中收集了社会人口统计学和临床特征的数据。我们将根据当前文献和利益相关者的咨询意见,确定模型的候选预测因素。模型开发将使用所有数据,根据可用样本量和事件率限制候选预测因素的数量。基于惩罚回归,针对 TRS 风险预测模型,采用缺失数据的多重插补法处理。内部验证将通过自举法进行,获得模型性能的乐观调整估计。将通过净效益和决策曲线(与竞争策略相比)评估模型在临床相关风险阈值方面的临床实用性。将与患者和临床利益相关者进行咨询,以确定治疗决策的潜在风险阈值。通过总共 20 名早期干预服务的临床医生进行的定性焦点小组,探索将模型嵌入临床工具的可接受性。

伦理和传播

预后模型的开发将基于现有队列的匿名数据,这些数据已经获得伦理批准。我们已经从斯塔福德和伦敦的早期干预服务机构招募了临床医生,并通过在线平台进行了焦点小组讨论。该研究已获得基尔大学精神病早期干预服务机构的定性焦点小组研究伦理批准(注册号:MH-210174)。为参与访谈或焦点小组的国民保健服务人员制定了合适的知情同意程序。我们已经准备并获得了当地伦理委员会的研究信息表、附函和同意书。研究结果将通过同行评审的出版物、会议报告和社交媒体进行分享。研究结果将通过同行评审的出版物、会议报告和社交媒体进行分享。一份通俗易懂的摘要将发布在合作网站上。

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Clozapine as a first- or second-line treatment in schizophrenia: a systematic review and meta-analysis.
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