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Original Investigation
December 2016

Deconstructing Pretest Risk Enrichment to Optimize Prediction of Psychosis in Individuals at Clinical High Risk

Author Affiliations
  • 1King's College London, Institute of Psychiatry, Psychology, and Neuroscience, London, United Kingdom
  • 2Outreach and Support in South London service, South London and the Maudsley National Health Service Foundation Trust, London, United Kingdom
  • 3Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
  • 4Department of Neurosciences, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
  • 5South London and the Maudsley National Health Service Foundation Trust, Biomedical Research Centre Nucleus, London, United Kingdom
JAMA Psychiatry. 2016;73(12):1260-1267. doi:10.1001/jamapsychiatry.2016.2707
Key Points

Question  What are the factors modulating pretest risk of psychosis onset in help-seeking individuals referred for clinical assessment on suspicion of psychosis risk?

Findings  This cohort study of 710 individuals assessed for suspected psychosis risk indicated substantial 6-year pretest risk enrichment (15%) and provided a stratification model that is based on race/ethnicity and source of referral.

Meaning  Stratification of pretest risk enrichment in individuals undergoing assessment for suspected psychosis risk may inform outreach campaigns and subsequent testing and eventually optimize psychosis prediction.

Abstract

Importance  Pretest risk estimation is routinely used in clinical medicine to inform further diagnostic testing in individuals with suspected diseases. To our knowledge, the overall characteristics and specific determinants of pretest risk of psychosis onset in individuals undergoing clinical high risk (CHR) assessment are unknown.

Objectives  To investigate the characteristics and determinants of pretest risk of psychosis onset in individuals undergoing CHR assessment and to develop and externally validate a pretest risk stratification model.

Design, Setting, and Participants  Clinical register-based cohort study. Individuals were drawn from electronic, real-world, real-time clinical records relating to routine mental health care of CHR services in South London and the Maudsley National Health Service Trust in London, United Kingdom. The study included nonpsychotic individuals referred on suspicion of psychosis risk and assessed by the Outreach and Support in South London CHR service from 2002 to 2015. Model development and validation was performed with machine-learning methods based on Least Absolute Shrinkage and Selection Operator for Cox proportional hazards model.

Main Outcomes and Measures  Pretest risk of psychosis onset in individuals undergoing CHR assessment. Predictors included age, sex, age × sex interaction, race/ethnicity, socioeconomic status, marital status, referral source, and referral year.

Results  A total of 710 nonpsychotic individuals undergoing CHR assessment were included. The mean age was 23 years. Three hundred ninety-nine individuals were men (56%), their race/ethnicity was heterogenous, and they were referred from a variety of sources. The cumulative 6-year pretest risk of psychosis was 14.55% (95% CI, 11.71% to 17.99%), confirming substantial pretest risk enrichment during the recruitment of individuals undergoing CHR assessment. Race/ethnicity and source of referral were associated with pretest risk enrichment. The predictive model based on these factors was externally validated, showing moderately good discrimination and sufficient calibration. It was used to stratify individuals undergoing CHR assessment into 4 classes of pretest risk (6-year): low, 3.39% (95% CI, 0.96% to 11.56%); moderately low, 11.58% (95% CI, 8.10% to 16.40%); moderately high, 23.69% (95% CI, 16.58% to 33.20%); and high, 53.65% (95% CI, 36.78% to 72.46%).

Conclusions and Relevance  Significant risk enrichment occurs before individuals are assessed for a suspected CHR state. Race/ethnicity and source of referral are associated with pretest risk enrichment in individuals undergoing CHR assessment. A stratification model can identify individuals at differential pretest risk of psychosis. Identification of these subgroups may inform outreach campaigns and subsequent testing and eventually optimize psychosis prediction.

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