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Is It Possible to Predict the Future in First-Episode Psychosis?

Jaana Suvisaari¹, Outi Mantere^1,2,3,4, Jaakko Keinänen^1,4

¹Mental Health Unit, National Institute for Health and Welfare, Helsinki, Finland.

Frontiers in Psychiatry

|November 29, 2018

Summary

Predicting outcomes in first-episode psychosis (FEP) is possible using clinical, social, and genetic factors. Combining these markers with machine learning can personalize treatment for better patient outcomes.

Keywords:

comorbidities first-episode psychosis mortality prediction recovery remission

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Area of Science:

Psychiatry
Neuroscience
Genetics

Background:

First-episode psychosis (FEP) outcomes vary widely, impacting treatment decisions.
Predicting patient trajectories is crucial for tailoring antipsychotic and psychosocial interventions.

Purpose of the Study:

To review prognostic markers for predicting outcomes in FEP.
To identify clinical, sociodemographic, cognitive, imaging, genetic, and biomarker predictors.
To explore the utility of machine learning for personalized FEP treatment.

Main Methods:

Selective literature review of prognostic markers in FEP.
Analysis of clinical, sociodemographic, cognitive, neuroimaging, genetic, and blood-based biomarkers.
Discussion of machine learning and other methodologies for predictive modeling.

Main Results:

Several clinical factors (e.g., longer duration of untreated psychosis, negative symptoms, SUDs) predict worse outcomes.
Sociodemographic factors like male gender and social disadvantage may also be relevant.
Cognitive function correlates with functional outcomes; genetic markers (PRSs, pharmacogenetics) show promise; neuroimaging and blood biomarkers require further validation.

Conclusions:

Prognostic markers can predict FEP outcomes to some extent, but no single marker is sufficient.
Personalized FEP treatment necessitates robust predictive tools, potentially integrating multiple markers.
Machine learning and novel biomarkers may enhance prediction accuracy and clinical utility for computer-assisted applications.