implementation-real-time-psychosis-risk-detection-alerting-system

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful ...

King's College London

The past 20 years have seen the evolution of the construct of a clinical high-risk (hereafter, HR) state for psychosis. This construct is designed to capture the pre-psychotic phase. Some aspects of this approach, such as its feasibility in children and adolescents, are still under investigation. In the present study, we address the feasibility of implementing prodrome clinics for HR individuals within the framework of Italy's national child and adolescent neuropsychiatry services and the clinical relevance of a HR diagnosis in this population. Using the Comprehensive Assessment of At-Risk Mental States (CAARMS) to identify help-seeking patients meeting at least one HR criterion at baseline (HR+), we recruited 50 subjects for a feasibility study. The results obtained show that the Italian version of the CAARMS is easily administrable, causing patients no substantial discomfort. The prevalence of HR+ in our cohort was 44 %, which increased by an additional 18 % when negative symptoms were considered as an experimental inclusion criterion (HRNeg). The HR+ subjects were significantly more impaired in their social and occupational functioning than their HR- peers (subjects not at HR). The cumulative 1-year transition risk of psychosis of the HR+ group was 26.7 %. When the HRNeg group was added, the 1-year transition risk was 17.3 %. We suggest that administration of the CAARMS to children and adolescents with putative prodromal psychosis is feasible and that this assessment can easily be integrated into existing Italian neuropsychiatry services although clinicians should interpret results with caution as results in this age group still have to be replicated.

Identifying children and adolescents at ultra high risk of psychosis in Italian neuropsychiatry services: a feasibility study.

 Primary indicated prevention in individuals at-risk for psychosis has the potential to improve the outcomes of this disorder. The ability to detect the majority of at-risk individuals is the main barrier toward extending benefits for the lives of many adolescents and young adults. Current detection strategies are highly inefficient. Only 5% (standalone specialized early detection services) to 12% (youth mental health services) of individuals who will develop a first psychotic disorder can be detected at the time of their at-risk stage. To overcome these challenges a pragmatic, clinically-based, individualized, transdiagnostic risk calculator has been developed to detect individuals at-risk of psychosis in secondary mental health care at scale. This calculator has been externally validated and has demonstrated good prognostic performance. However, it is not known whether it can be used in the real world clinical routine. For example, clinicians may not be willing to adhere to the recommendations made by the transdiagnostic risk calculator. Implementation studies are needed to address pragmatic challenges relating to the real world use of the transdiagnostic risk calculator. The aim of the current study is to provide  and  feasibility data to support the implementation of the transdiagnostic risk calculator in clinical routine.  This is a study which comprises of two subsequent phases: an  phase of 1 month and an  phase of 11 months. The  phase aims at developing and integrating the transdiagnostic risk calculator in the local electronic health register (primary outcome). The  phase aims at addressing the clinicians' adherence to the recommendations made by the transdiagnostic risk calculator (primary outcome) and other secondary feasibility parameters that are necessary to estimate the resources needed for its implementation.  This is the first implementation study for risk prediction models in individuals at-risk for psychosis. Ultimately, successful implementation is the true measure of a prediction model's utility. Therefore, the overall translational deliverable of the current study would be to extend the benefits of primary indicated prevention and improve outcomes of first episode psychosis. This may produce significant social benefits for many adolescents and young adults and their families.

Real World Implementation of a Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk of Psychosis in Clinical Routine: Study Protocol.

Prenatal and perinatal insults are implicated in the aetiopathogenesis of psychotic disorders but the consistency and magnitude of their associations with psychosis have not been updated for nearly two decades. The aim of this systematic review and meta-analysis was to provide a comprehensive and up-to-date synthesis of the evidence on the association between prenatal or perinatal risk and protective factors and psychotic disorders.

Prenatal and perinatal risk and protective factors for psychosis: a systematic review and meta-analysis.

The Psychosis Polyrisk Score (PPS) is a potential biomarker integrating non-purely genetic risk/protective factors for psychosis that may improve identification of individuals at risk and prediction of their outcomes at the individual subject level. Biomarkers that are easy to administer are direly needed in early psychosis to facilitate clinical implementation. This study digitally implements the PPS and pilots its feasibility of use in the real world.

Giulia Spada

Early Psychosis: Interventions and Clinical-detection (EPIC) lab,
Department of Psychosis Studies,
Institute of Psychiatry,
Psychology & Neuroscience,
Early Psychosis: Interventions and Clinical-detection (EPIC) lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience

King's College London

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack