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

Abstract

IntroductionTreatment-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 analysisWe 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 disseminationThe 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.

Discharge destinations for young people with a first episode of psychosis after attending an early intervention for psychosis service

“…Two of the core objectives of early intervention services are to achieve timely remission of psychotic symptoms and prevent relapse ( Stavely et al, 2013 ). Within a period of 12–16 weeks, it is possible to have trials of two different antipsychotic medications, and therefore even at this stage, treatment resistance could be identified ( Lally et al, 2016 ) and work has commenced on a prediction tool to identify individuals with treatment resistance ( Farooq et al, 2022 ). At this point, individuals with a diagnosis of treatment-resistant schizophrenia could be considered for clozapine.…”

Intelligent meteorological information acquisition service platform based on Internet of Things

“…However, for Chinese people, new meteorological and management challenges are still expensive, information service systems are still relatively inadequate, and meteorological information services are often targeted at specific areas. Specific applications and specific data resources lead to the increasing complexity of data development and use, the lack of effective analysis and improvement, the low efficiency of transforming data resources into useful information, and the mismatch between the increasing data resources of meteorological departments and the relatively lack of meteorological information services 1,2 . This paper discusses the IoT and IoT meteorological data collection and extraction techniques to realize the identification of smart meteorological data.…”

Comparative Analysis of Combination Therapy Regimens in Patients with Paranoid Schizophrenia with Non-Suicidal Auto aggression and Identified Signs of Resistance to Ongoing Neuroleptic Monotherapy

“…The increase in the number of therapeutically resistant patients with paranoid schizophrenia with auto aggression remains one of the most pressing problems in psychiatry remains as the most common nosological unit [1,2,3]. At the same time, a large amount of research material has been accumulated on the factors and conditions that affect the formation of the state of resistance in such patients [4,5,6,7]. First of all, these include: a debut at an early age, an "erased" beginning, a continuous course, the dominance of negative symptoms in the structure of the pathological process, and the fading of the affective component.…”