PROJECT SUMMARY/ABSTRACT
Trajectories and Predictors in the Clinical High Risk for Psychosis Population: Prediction Scientific
Global Consortium (PRESCIENT)
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 significant 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 and introduce validated tools for use in clinical practice. This 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 has three overarching aims:
1. CHR Network Consolidation: Use the Australian Early Psychosis Collaborative Consortium (AEPCC)
national platform to consolidate a network of CHR recruitment centres organised according to a ‘hub and spoke’
model, with Orygen functioning as the central hub with 2 Australian and 8 non-Australian clinics as spokes. The
network will: recruit a large sample of CHR patients (n=937) and a healthy control (n=250) sample; conduct
repeat multimodal assessments; map trajectories and outcomes over a 2 year period (conversion to psychotic
disorder, persistent and incident non-psychotic disorders, non-remission of CHR status, persistent negative
symptoms, psychosocial functioning, full recovery). This international network of CHR recruitment centres will
provide the clinical research infrastructure for future treatment trials in this clinical population informed by findings
from the current program of work.
2. Prediction: In collaboration with the Data Processing, Analysis and Coordination Centre (DPACC), the
PRESCIENT dataset will be used to:
2a. Test the external validity of existing and forthcoming prediction models in the field (e.g., NAPLS, EU-GEI,
PRONIA, PSYSCAN).
2b. Implement the model with strongest performance as an online risk calculator that can be calibrated for service
setting (primary vs specialist settings) and availability of type of data (e.g. clinical data alone, clinical data plus
neurophysiological data, polygenic risk score, etc).
2c. Use the Accelerating Medicines Partnership in Schizophrenia (AMP SCZ) dataset (combined PRESCIENT
and ProNET data) to develop new, more refined prediction models and risk calculators using recent theoretical
and methodological advances (e.g., dynamic prediction, probabilistic multimodal modelling) and a range of
biomarkers. These tools will be of clinical utility in decision making about stepping interventions up/down as risk
is assessed over time (clinical trajectory, treatment response) and in response to incoming biomarker
information, as well as guide stratification of patients in future clinical trials.
3. Validation: Internally and externally validate the prediction models generated using the AMP SCZ dataset.
This will test the robustness, replicability, and generalizability of the models’ performance.
