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Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

Smart, Sophie E.; Agbedjro, Deborah; Pardiñas, Antonio F.; Ajnakina, Olesya; Alameda, Luis; Andreassen, Ole A.; Barnes, Thomas R.E.; Berardi, Domenico; Camporesi, Sara; Cleusix, Martine; Conus, Philippe; Crespo-Facorro, Benedicto; D'Andrea, Giuseppe; Demjaha, Arsime; Di Forti, Marta; Do, Kim; Doody, Gillian; Eap, Chin B.; Ferchiou, Aziz; Guidi, Lorenzo; Homman, Lina; Jenni, Raoul; Joyce, Eileen; Kassoumeri, Laura; Lastrina, Ornella; Melle, Ingrid; Morgan, Craig; O'Neill, Francis A.; Pignon, Baptiste; Restellini, Romeo; Richard, Jean-Romain; Simonsen, Carmen; Španiel, Filip; Szöke, Andrei; Tarricone, Ilaria; Tortelli, Andrea; Üçok, Alp; Vázquez-Bourgon, Javier; Murray, Robin M.; Walters, James T.R.; Stahl, Daniel; MacCabe, James H.


Sophie E. Smart

Deborah Agbedjro

Antonio F. Pardiñas

Olesya Ajnakina

Luis Alameda

Ole A. Andreassen

Thomas R.E. Barnes

Domenico Berardi

Sara Camporesi

Martine Cleusix

Philippe Conus

Benedicto Crespo-Facorro

Giuseppe D'Andrea

Arsime Demjaha

Marta Di Forti

Kim Do

Professor of General Adult Psychiatry and Medical Education

Chin B. Eap

Aziz Ferchiou

Lorenzo Guidi

Lina Homman

Raoul Jenni

Eileen Joyce

Laura Kassoumeri

Ornella Lastrina

Ingrid Melle

Craig Morgan

Francis A. O'Neill

Baptiste Pignon

Romeo Restellini

Jean-Romain Richard

Carmen Simonsen

Filip Španiel

Andrei Szöke

Ilaria Tarricone

Andrea Tortelli

Alp Üçok

Javier Vázquez-Bourgon

Robin M. Murray

James T.R. Walters

Daniel Stahl

James H. MacCabe


Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR. We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction. Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %). Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.


Smart, S. E., Agbedjro, D., Pardiñas, A. F., Ajnakina, O., Alameda, L., Andreassen, O. A., …MacCabe, J. H. (2022). Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium. Schizophrenia Research, 250, 1-9.

Journal Article Type Article
Acceptance Date Sep 4, 2022
Online Publication Date Oct 12, 2022
Publication Date 2022-12
Deposit Date Nov 21, 2022
Publicly Available Date Nov 21, 2022
Journal Schizophrenia Research
Print ISSN 0920-9964
Electronic ISSN 1573-2509
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 250
Pages 1-9
Keywords Prospective longitudinal cohort, Stratification, Treatment resistant schizophrenia, First episode psychosis, Prediction modelling, Machine learning
Public URL
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