Ajnakina, O;
Fadilah, I;
Quattrone, D;
Arango, C;
Berardi, D;
Bernardo, M;
Bobes, J;
... Stahl, D; + view all
(2023)
Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case–control Study and Modern Statistical Learning Methods.
Schizophrenia Bulletin Open
, 4
(1)
, Article sgad008. 10.1093/schizbullopen/sgad008.
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Abstract
Background and Hypothesis: It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. / Study Design: We used data from a large multi-center study encompassing 2627 phenotypically well-defined participants (aged 18-64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularization by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. / Study Results: Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC=0.84-0.86). Specificity (range=73.9-78.0%) and sensitivity (range=75.6-79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. / Conclusions: The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizability.
| Type: | Article |
|---|---|
| Title: | Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case–control Study and Modern Statistical Learning Methods |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/schizbullopen/sgad008 |
| Publisher version: | https://doi.org/10.1093/schizbullopen/sgad008 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2023. Published by Oxford University Press on behalf of the University of Maryland's school of medicine, Maryland Psychiatric Research Center. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. |
| Keywords: | psychosis/diagnostic factors, diagnostic prediction modeling/risk prediction, cannabis use |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry > Epidemiology and Applied Clinical Research |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10178290 |
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