Psychosis prediction: stratification of risk estimation with information-processing and premorbid functioning...

Area of Science:

• Neuroscience
• Psychiatry
• Clinical Psychology

Background:

• The period before the first psychotic episode presents a critical window for intervention.
• Developing accurate prediction models for first psychosis is crucial for individualized risk assessment.

Purpose of the Study:

• To develop an optimized prediction model for first psychosis using diverse data sources.
• To enable individualized risk estimation for individuals clinically at high risk (CHR) for psychosis.

Main Methods:

• Sixty-one CHR subjects from the Dutch Prediction of Psychosis Study were assessed.
• Data collected included neuropsychology, symptomatology, environmental factors, premorbid adjustment, and neurophysiology.
• A 36-month follow-up period was used to track transitions to psychosis.

Main Results:

• Eighteen participants (29.5%) transitioned to psychosis within 36 months.
• Premorbid adjustment and parietal P300 amplitude were significant predictors (HR=2.13 and HR=1.27, respectively).
• The prognostic score demonstrated high sensitivity (88.9%) and specificity (82.5%), with an AUC of 0.91.

Conclusions:

• Predicting first psychosis in CHR subjects can be enhanced by incorporating premorbid adjustment and information-processing variables.
• A multistep algorithm combining risk detection and stratification improves prediction accuracy.
• The developed model effectively identifies individuals at high risk for psychosis, facilitating timely interventions.