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Updated: Aug 1, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Understanding Lifelong Factors and Prediction Models of Social Functioning After Psychosis Onset Using the
Natalia Tiles-Sar1,2, Tesfa Dejenie Habtewold1, Edith J Liemburg2
1Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Background And Hypothesis:
Current rates of poor social functioning (SF) in people with psychosis history reach 80% worldwide. We aimed to identify a core set of lifelong predictors and build prediction models of SF after psychosis onset.
Study Design:
We utilized data of 1119 patients from the Genetic Risk and Outcome in Psychosis (GROUP) longitudinal Dutch cohort. First, we applied group-based trajectory modeling to identify premorbid adjustment trajectories. We further investigated the association between the premorbid adjustment trajectories, six-year-long cognitive deficits, positive, and negative symptoms trajectories, and SF at 3-year and 6-year follow-ups. Next, we checked associations between demographics, clinical, and environmental factors measured at the baseline and SF at follow-up. Finally, we built and internally validated 2 predictive models of SF.
Study Results:
We found all trajectories were significantly associated with SF (P < .01), explaining up to 16% of SF variation (R2 0.15 for 3- and 0.16 for 6-year follow-up). Demographics (sex, ethnicity, age, education), clinical parameters (genetic predisposition, illness duration, psychotic episodes, cannabis use), and environment (childhood trauma, number of moves, marriage, employment, urbanicity, unmet needs of social support) were also significantly associated with SF. After validation, final prediction models explained a variance up to 27% (95% CI: 0.23, 0.30) at 3-year and 26% (95% CI: 0.22, 0.31) at 6-year follow-up.
Conclusions:
We found a core set of lifelong predictors of SF. Yet, the performance of our prediction models was moderate.
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