Extended Joint Models for Prediction of Psychosis Onset in High-Risk Patients

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Researchers claim that dynamic risk calculator models that are used online show a higher prognostic accuracy than static models for extended joint modeling in the prediction of psychosis in patients who are at high risk for psychosis.

The use of extended joint models has been shown to slightly increase the prognostic accuracy of predicting outcomes in patients at high risk for psychosis compared with the use of basic joint models, and dynamic models demonstrate higher prognostic accuracy than static models, according to study results published in Schizophrenia Bulletin.

The analysis was conducted in patients at high risk for psychosis who were recruited as part of the Basel Früherkennung von Psychosen study — an open prospective clinical study of all consecutive referrals to a specialized outpatient clinic for the early detection of psychosis at the Psychiatric University Hospital Basel in Switzerland.

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The investigators sought to develop and internally validate a dynamic risk prediction model (ie, a joint model) and to implement this model in a user-friendly online risk calculator. In addition, they endeavored to study the prognostic performance of extended dynamic risk prediction models and to compare the use of static vs dynamic prediction. They recruited a total of 196 patients at high risk for psychosis as part of the study. They used the Brief Psychiatric Rating Scale-Expanded to evaluate psychopathology and transition to psychosis at regular intervals for up to 5 years.

Study results showed that of the 196 enrolled individuals, 42 (21%) in the clinical high risk for psychosis group transitioned to psychosis within the 5-year follow-up period. At 1, 2, 3, 4, and 5 years, Kaplan-Meier transition risks were 16.3%, 19.2%, 22.5%, 24.8%, and 30.4%, respectively. The mean follow-up time was 1.2 years in patients at high risk for psychosis and had transitioned and 2.7 years in individuals who were at high risk for psychosis but did not transition to psychosis.

The study claims to be the first of its kind to develop and validate a dynamic prognostic model for predicting the onset of psychosis in individuals at high risk for psychosis over a relatively long follow-up of 5 years. A total of 60 different dynamic prognostic models were compared with each other and with a static prognostic model with respect to their internally validated prognostic performance.

The investigators concluded that using extended specifications of joint models improved the prognostic performance of the models. Study findings also showed that dynamic prediction performed better than static prediction.


Studerus E, Beck K, Fusar-Poli P, Riecher-Rössler A. Development and validation of a dynamic risk prediction model to forecast psychosis onset in patients at clinical high risk [published online July 29, 2019]. Schizophr Bull. doi:10.1093/schbul/sbz059