Psychosis prediction: Stratification of risk estimation with information-processing and premorbid functioning variables

Abstract

Background: The period preceding the first psychotic episode is regarded as a promising period for intervention. We aimed to develop an optimized prediction model of a first psychosis, considering different sources of information. The outcome of this model may be used for individualized risk estimation. Methods: Sixty-one subjects clinically at high risk (CHR), participating in the Dutch Prediction of Psychosis Study, were assessed at baseline with instruments yielding data on neuropsychology, symptomatology, environmental factors, premorbid adjustment, and neurophysiology. The follow-up period was 36 months. Results: At 36 months, 18 participants (29.5%) had made a transition to psychosis. Premorbid adjustment (P =. 001, hazard ratio [HR] = 2.13, 95% CI = 1.39/3.28) and parietal P300 amplitude (P =. 004, HR = 1.27, 95% CI = 1.08/1.45) remained as predictors in the Cox proportional hazard model. The resulting prognostic score (PS) showed a sensitivity of 88.9% and a specificity of 82.5%. The area under the curve of the PS was 0.91 (95% CI = 0.83-0.98, cross-validation: 0.86), indicating an outstanding ability of the model to discriminate between transition and nontransition. The PS was further stratified into 3 risk classes establishing a prognostic index. In the class with the worst social-personal adjustment and lowest P300 amplitudes, 74% of the subjects made a transition to psychosis. Furthermore, transition emerged on average more than 17 months earlier than in the lowest risk class. Conclusions: Our results suggest that predicting a first psychotic episode in CHR subjects could be improved with a model including premorbid adjustment and information-processing variables in a multistep algorithm combining risk detection and stratification.