Multivariate joint models for the dynamic prediction of psychosis in individuals with clinical high risk.

This study attempted to construct and validate dynamic prediction via multivariate joint models and compare the prognostic performance of these models to both static and univariate joint models. Individuals with clinical high risk(CHR)(n = 289) were recruited and re-assessed for positive symptoms, general functions, and conversion to psychosis at 2-months, 1-year, and 2-years to develop the dynamic models. A multivariate joint model of positive psychotic symptoms was assessed using the Structured Interview for Prodromal Symptoms(SIPSp) and general function assessed by global assessment of functioning scores(GAFs) with time-to-conversion to psychosis. The area under the receiver operating characteristic(ROC) curve(AUC) was used to test the accuracy of the models. Among 298 CHR individuals, 68 converted to psychosis within 2 years after the initial assessments. Multivariate joint models showed that declining GAFs and increasing SIPSp corresponded to significant and trending to significantly increased risk of psychosis onset and had much higher prognostic accuracy (cross-validated AUC=0.9) compared to the static model(AUC=0.6) and univariate joint models(cross-validated AUC=0.6-0.8). Our results showed that multivariate joint models could be highly efficient in forecasting psychosis onset for CHR individuals. Longitudinal assessments for psychopathology and general functions can be useful for dynamically predicting the prognosis of the pre-morbid phase of psychosis.