Multivariate prediction of emerging psychosis in adolescents at high risk for schizophrenia.

backgroundAccurate prediction of psychosis development in high-risk populations is an important but thus far elusive goal. Of the many diverse etiologic and risk factors identified thus far, few have been combined into prospective multivariate risk ascertainment models. We tested the predictive power of familial, neurobiological, socioenvironmental, cognitive and clinical risk factors through an integrative biopsychosocial model for emerging psychosis in young relatives at familial risk for schizophrenia.

methods96 young first- and second- degree relatives of schizophrenia probands were followed for an average of 2.38 (SD=0.98) years to examine their trajectory towards psychosis. Iterative structural equation modelling utilizing multiple etiologic and risk factors was employed to estimate their joint contribution to prediction of psychosis development.

resultsThe rate of conversion to psychosis over the study period was 12.5%. In the final model, clinical measures of schizotypy were directly predictive of conversion, with early (familial, biological, socioenvironmental) and cognitive risk factors indirectly predictive of psychosis through increased baseline clinical symptomatology. Our model provided an excellent fit to the observed data, with sensitivity of 0.17, specificity of 0.99, positive predictive value of 0.67 and negative predictive value of 0.89.

conclusionsIntegrative modeling of multivariate data from familial, neurobiological, socioenvironmental, cognitive and clinical domains represents a powerful approach to prediction of psychosis development. The high specificity and low sensitivity found using a combination of such variables suggests that their utility may be in confirmatory testing among already selected high-risk individuals, rather than for initial screening. These findings also highlight the importance of data from a broad array of etiologic and risk factors, even within a familial high-risk population. With further refinement and validation, such methods could form key components of early detection, intervention and prevention programs.