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

Abstract

Background: 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 in-vitro and in-vivo feasibility data to support the implementation of the transdiagnostic risk calculator in clinical routine.Method: This is a study which comprises of two subsequent phases: an in-vitro phase of 1 month and an in-vivo phase of 11 months. The in-vitro phase aims at developing and integrating the transdiagnostic risk calculator in the local electronic health register (primary outcome). The in-vivo 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.Discussion: 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.

Preventive Treatments for Psychosis: Umbrella Review (Just the Evidence)

“…It is therefore clear that we need to improve our ability to detect CHR-P individuals in secondary mental health care, primary care, and in the community. Some experimental approaches have been validated, which include automatic risk calculators for use in mental health trusts (57,103,105) or in the community (106).…”

Transdiagnostic individualized clinically-based risk calculator for the automatic detection of individuals at-risk and the prediction of psychosis: external replication in 2,430,333 US patients

“…Psychiatry is undergoing a digital revolution 39 , and there is an ongoing expansion of EHR adoption worldwide. More to this point, this risk calculator was evidently developed with a clear vision of future implementation as decision support in clinical routine and is currently being piloted in this capacity 12 , 15 . For example, it uses simple predictors that can easily be understood by clinicians, as compared to complex black-box machine-learning-derived algorithms 40 .…”

“…Since this model was originally developed on a retrospective cohort 7 , it excluded cases with an onset of psychosis within the first three months to minimize the short-term diagnostic instability of baseline ICD-10 index diagnoses. However, during the subsequent implementation study 12 , 15 an updated version of the model was adapted for prospective use (i.e., not excluding transitions occurring in the first three months), demonstrating similar prognostic performance (Table S4 ). Furthermore, a lookback period was additionally used in this study (see Methods S3), to minimize the risk of misclassification of index diagnosis date.…”

“…This prognostic model has shown adequate performance in a first external validation in the SLaM boroughs of Lewisham and Croydon ( n = 54,716, Harrell’s C = 0.79) 7 and in a second external validation in the Camden and Islington NHS Foundation Trust (C&I; n = 13,702, Harrell’s C = 0.73) 11 , with Harrell’s C demonstrating the probability that a randomly selected patient who experienced an event will have a higher score than a patient who did not. This prognostic model is also currently being piloted for real-world implementation in clinical routine in the UK 12 .…”

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

“…Early interventions in psychosis can improve outcomes of this mental disorder 4 . In particular, detection, prognostic assessment and preventive treatment of individuals who are at clinical high risk for developing psychosis (CHR-P) 5 provides a unique potential to alter the course of the disorder, thereby improving the quality of life for many people and their families 3,6 . CHR-P individuals are help-seeking young people presenting with attenuated symptoms and functional impairment 7 : their risk of developing psychosis is 20% at 2-years 8 but it is higher in some specific subgroups 9,10 .…”