Combining clinical with cognitive or MRI data for predicting transition to psychosis in ultra-high risk patients: Data from the PACE 400 cohort

BACKGROUND: Multimodal modelling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals at ultra high risk (UHR). Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in grey matter volume in multiple brain regions identified by magnetic resonance imaging (MRI). METHODS: In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality - cognition, cortical structure information, and the neuroanatomical measure of "brain age gap" - to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well characterised cohort of Australian youth identified as UHR using the Comprehensive Assessment of At Risk Mental States followed for up to 18 years, containing clinical data (from N=416 participants), cognitive data (N=213), and MRI cortical parameters extracted using Freesurfer (N=231). RESULTS: The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0-12%, brain age gap 7%, cognition 0-16%). CONCLUSIONS: In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in fit of the model for long term prediction of transition to psychosis.