Discovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk

Abstract

In the past 2 to 3 decades, clinicians have used the clinical high risk for psychosis (CHR-P) paradigm to better understand factors that contribute to the onset of psychotic disorders. While this paradigm is useful to identify individuals at risk, the CHR-P criteria are not sufficient to predict outcomes from the CHR-P population. Because approximately 25% of the CHR-P population will ultimately convert to psychosis, more precise methods of prediction are needed to account for heterogeneity in both risk factors and outcomes in the CHR-P population. To this end, several groups in recent years have used data-driven approaches to refine predictive algorithms to predict both conversion to psychosis and functional outcomes. These models have generally used either clinical and behavioral data, including demographics and measures of symptom severity, neurocognitive functioning, and social functioning, or neuroimaging data, including structural and functional measures, to predict conversion to psychosis in CHR-P samples. This review focuses on the empirical models that have been derived within each of these lines of research and evaluates the performance and methodology of these models. This review also serves to inform best practices for data-driven approaches and directions moving forward to improve our prediction of psychotic disorders and associated outcomes. Because sample size is still the most critical consideration in the current models, we urge that algorithms to predict conversion be conducted using multisite data in order to obtain the power necessary to conclusively determine predictive accuracy without overfitting.

Keywords: Clinical high risk; Machine learning; Neuroimaging; Predictive models; Psychosis; Risk prediction.