Living Evidence onFirst-EpiSodeof psychosis Treatments and Outcomes (EFESTO): a living individual participant data network meta-analysis and learning

To overcome the limited power of current detection strategies for psychosis-risk, the EPIC lab at IoPPN has recently leveraged precision medicine methods and developed clinical prediction models, including the 'transdiagnostic risk calculator'. This model indexes the probability of transition to psychosis (i.e. the risk of the participant developing a first psychotic disorder over time) within secondary mental health care (1). This model was based on preselected clinically based variables which are widely available in electronic health records (EHRs): index ICD-10/At-Risk Mental State (ARMS) diagnosis, age, sex, age by sex, and race/ethnicity. The model was termed "transdiagnostic" because it predicts the onset of psychosis not only from a CHR-P stage but also from other non-psychotic mental disorders such as affective mental disorders. Indeed, there is accumulating evidence showing that the majority of CHR-P individuals would also meet criteria for a secondary diagnosis of co-morbid mental disorder, mostly depression and anxiety (2). Moreover, bipolar mood disorders can also predate the onset of psychotic disorders (1).The transdiagnostic risk calculator was externally validated in a large clinical registry cohort of adults in the SLaM boroughs of Lewisham and Croydon (n=54,716) and showed good external accuracy (Harrell's C=0.79). Further replications have demonstrated its transportability. Specifically, the transdiagnostic risk calculator has been externally validated in three independent EHR datasets of different population profiles, demonstrating adequate prognostic performance. Firstly, with the Camden and Islington NHS Foundation Trust (n=13,702, Harrell's C=0.73) (3), which comprised an older population relative to the one used to develop the calculator. Secondly, with the Oxford Health NHS Foundation Trust (n=33,710, Harrell's C=0.79), whichcontained a mix of both urban and rural backgrounds (4).Thirdly, the calculator was externally validated internationally in the IBM Commercial database (n=2,430,333, Harrell's C=0.68), retaining an accuracy above chance despite the lack of ethnicity data in this replication (5), which represents the largest external replication of clinical prediction models in psychiatry. In a further step, this clinical prediction model has been refined with Natural Language Processing applications to automatically screen the text of clinical notes and letters. This enables the inclusion of more fine-grained predictors such as symptom/substance misuse predictors (tearfulness, poorappetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia). NLP methods are based on data-mining artificial intelligence and represent bespoke analytic techniques available at the IoPPN. This refinement further improved the model significantly to Harrell's C=0.85 (95% CI 0.84-0.86), an increase of 0.06 from the original model. The above findings show that the risk calculator provides a potential tool that could improve detection of individuals at risk of psychosis. However there is a dearth of implementation science in psychiatry and only less than 1% of published clinical prediction models are eventually implemented in clinical practice (6). Despite this, the real-world implementation of the transdiagnostic risk calculator was tested in a pilot study conducted at the IoPPN (7), with about 77% of clinicians respondingto prompts issued by the risk calculator, indicating a good level of adherence.