Psychotic illnesses usually first emerge in young people and result in widespread suffering, protracted disability, premature death, and a huge economic burden. Early intervention represents a vital strategy to reduce this burden. Psychotic disorders are preceded by a prodromal period of distress, impaired functioning and subthreshold symptomatology. Our original research operationally defined the Clinical High Risk (CHR) state, which predicts a substantially increased risk of incipient psychosis. There is substantial heterogeneity in clinical trajectories in the CHR population. The field is currently unable to reliably identify these trajectories early on, particularly on an individual patient level. The models to date (using clinical, neurocognitive, neuroimaging, neurobiological and genetic data) have yielded only modest predictive value for conversion to psychotic disorder and other outcomes. This presents a challenge for targeted intervention development and developing robust aetiological models. The current project seeks to develop more robust prediction models for a range of outcomes in the CHR population (conversion to psychotic disorder, persistent and incident non-psychotic disorder, non-remission of CHR status, persistent negative symptoms, full recovery, functional outcome) and introduce validated tools for use in clinical practice. These prediction models and associated clinical tools will be developed using multimodal data consisting of biomarkers (neuroimaging, neurocognition, neurophysiology, biospecimens), clinical data, and digital momentary assessments. The prediction models will facilitate selection of CHR patients for enrolment in clinical trials, serve as measures of early treatment effects, and monitor disease progression and clinical and functional outcomes. The project is based on four pillars:
1. An existing nationwide clinical infrastructure (network) to support recruitment and follow up of a large cohort of CHR young people (n=1000) over a short timeframe (2 year recruitment period, 2 year follow up), as well as a clinical comparison group (n=300).
2. Use of this dataset to: validate existing and forthcoming prediction models and develop new, more refined prediction models using recent methodological advances and exploratory biomarkers.
3. Recruitment of an independent CHR sample across international centres for external validation of models generated in the Australian network to ensure generalizability of findings. Alternatively, these sites could be used as additional spokes in the network, with alternative data sets used for external validation purposes (see 2.5.4). This network of sites and research specialization will provide the clinical research infrastructure for future treatment trials in this clinical population informed by findings of the current program of work.
4. Unique track record as pioneers of the CHR field and expertise in state-of-the-art predictive modelling, including pioneering new approaches to prediction (dynamic prediction, multimodal probabilistic prediction, network theory), and use of digital technologies to support collection of requisite data. We also have unrivalled track record in management of multisite research networks in this clinical population and rapid recruitment locally, nationally, and internationally.