Refining E-detectioN Tools for Emerging mental disoRders (ENTER) to identify young people at clinical high risk for psychosis in the community: model development and internal validation
- 1Department of Psychiatry, University of Oxford, Oxford, UK; NIHR Oxford Health Biomedical Research Centre, Oxford, UK; OPEN Early Detection Service, Oxford Heal
Abstract
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.
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BibTeX
@misc{oliver2026refining,
title = {{Refining E-detectioN Tools for Emerging mental disoRders (ENTER) to identify young people at clinical high risk for psychosis in the community: model development and internal validation}},
author = {D. Oliver and P. Fusar-Poli and G. Brunner and K. Haining and A. Estrade and M. Azis and B. Bianciardi and S. Cooper and S. Ramesh and P. Wallman and X. Liang and Y. Logeswaran and R. Stefanelli and S. Damiani and A. Melillo and U. Provenzani and P. McGuire and S. Brewster and S. Macdonald and P. D. C. San Vito and A. McConnachie and C. Haig and T. Spencer and K. Diederen and P. J. Uhlhaas},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.05.26364756},
url = {https://www.medrxiv.org/content/10.64898/2026.10.05.26364756v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Refining E-detectioN Tools for Emerging mental disoRders (ENTER) to identify young people at clinical high risk for psychosis in the community: model development and internal validation
AU - D. Oliver
AU - P. Fusar-Poli
AU - G. Brunner
AU - K. Haining
AU - A. Estrade
AU - M. Azis
AU - B. Bianciardi
AU - S. Cooper
AU - S. Ramesh
AU - P. Wallman
AU - X. Liang
AU - Y. Logeswaran
AU - R. Stefanelli
AU - S. Damiani
AU - A. Melillo
AU - U. Provenzani
AU - P. McGuire
AU - S. Brewster
AU - S. Macdonald
AU - P. D. C. San Vito
AU - A. McConnachie
AU - C. Haig
AU - T. Spencer
AU - K. Diederen
AU - P. J. Uhlhaas
PY - 2026
DA - 2026/10/05
PB - medRxiv
DO - 10.64898/2026.10.05.26364756
UR - https://www.medrxiv.org/content/10.64898/2026.10.05.26364756v1
AB - 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.
ER -