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

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.