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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Individualized pretest risk estimates to guide treatment decisions in patients with clinical high risk for psychotic

Elodie Sprüngli-Toffel¹, Erich Studerus², Logos Curtis³

¹General Psychiatry Service, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Geneva, Geneva, Switzerland.

Spanish Journal of Psychiatry and Mental Health

|September 20, 2024

Abstract

Introduction:

Clinical high risk for psychosis (CHR) states are associated with an increased risk of transition to psychosis. However, the predictive value of CHR screening interviews is dependent on pretest risk enrichment in referred patients. This poses a major obstacle to CHR outreach campaigns since they invariably lead to risk dilution through enhanced awareness. A potential compensatory strategy is to use estimates of individual pretest risk as a 'gatekeeper' for specialized assessment. We aimed to test a risk stratification model previously developed in London, UK (OASIS) and to train a new predictive model for the Swiss population.

Method:

The sample was composed of 513 individuals referred for CHR assessment from six Swiss early psychosis detection services. Sociodemographic variables available at referral were used as predictors whereas the outcome variable was transition to psychosis.

Results:

Replication of the risk stratification model developed in OASIS resulted in poor performance (Harrel's c=0.51). Retraining resulted in moderate discrimination (Harrel's c=0.67) which significantly differentiated between different risk groups. The lowest risk group had a cumulative transition incidence of 6.4% (CI: 0-23.1%) over two years.

Conclusion:

Failure to replicate the OASIS risk stratification model might reflect differences in the public health care systems and referral structures between Switzerland and London. Retraining resulted in a model with adequate discrimination performance. The developed model in combination with CHR assessment result, might be useful for identifying individuals with high pretest risk, who might benefit most from specialized intervention.

Keywords:

CHR Early detection Pretest risk Psychosis

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