Abstract / Summary
Early detection of individuals at clinical high risk for psychosis (CHR-P) improves access to preventive care. We developed and internally validated the E-DetectioN Tool for Emerging mental disoRders (ENTER), a multimodal digital screener designed to improve community identification of CHR-P compared with an established approach (PQ-16 cut-off). Participants aged 12-35 years from the UK and Italy completed the online ENTER screener, assessing attenuated psychotic symptoms, processing speed and environmental risk factors. Participants above the PQ-16 cut-off and a random subset below were assessed with the Comprehensive Assessment of At Risk Mental States (CAARMS). PQ-16 cut-off and ENTER models were developed using (penalised) logistic regression, random forest and extreme gradient boosting (XGBoost) within a repeated nested cross-validation framework with semi-supervised learning incorporating unlabelled data. Models were evaluated by discrimination, calibration and clinical utility. 2,522 participants completed the ENTER screener. 495 underwent CAARMS assessments. 124 (25.1%) met CHR-P criteria; 2,027 participants were not assessed with the CAARMS (unlabelled). ENTER showed improved discrimination (C=0.744, 95%CI: 0.697-0.790) compared to the PQ-16 cut-off (C=0.625, 95%CI: 0.570-0.682, p=0.003) and similar calibration (ENTER: slope=1.02, 95%CI: 0.78-1.32; PQ-16 cut-off: slope=0.39, 95%CI: 0.19-0.52, p=0.074). At a clinically relevant threshold, ENTER identified approximately 2 additional CHR-P individuals per 100 assessments compared with the PQ-16 cut-off. Compared with established PQ-16 cut-off pre-screening, ENTER showed higher performance for community identification of CHR-P individuals. Multimodal digital screening might support more informative community triage before specialist assessment. External validation and implementation studies are required to establish real-world clinical utility.