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.
Autor: | Ajnakina, Olesya, Fadilah, Ihsan, Quattrone, Diego, Arango, Celso, Berardi, Domenico, Bernardo, Miguel, Bobes, Julio, Haan, Lieuwe de, Del-Ben, Cristina Marta, Gayer-Anderson, Charlotte, Stilo, Simona, Jongsma, Hannah E, Lasalvia, Antonio, Tosato, Sarah, Llorca, Pierre-Michel, Menezes, Paulo Rossi, Rutten, Bart P, Santos, Jose Luis, Sanjuán, Julio, Selten, Jean-Paul |
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Předmět: |
STATISTICS
RESEARCH CANNABIS (Genus) SUBSTANCE abuse SAMPLE size (Statistics) CONFIDENCE intervals PSYCHOSES CASE-control method LEARNING strategies RISK assessment COMPARATIVE studies THEORY DESCRIPTIVE statistics PREDICTION models RECEIVER operating characteristic curves DATA analysis software ODDS ratio |
Zdroj: | Schizophrenia Bulletin Open; Jan2023, Vol. 4 Issue 1, p1-13, 13p |
Abstrakt: | 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 (ranges |
Databáze: | Complementary Index |
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