RT Journal Article
T1 Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium.
A1 Smart, Sophie E
A1 Agbedjro, Deborah
A1 Pardiñas, Antonio F
A1 Ajnakina, Olesya
A1 Alameda, Luis
A1 Andreassen, Ole A
A1 Barnes, Thomas R E
A1 Berardi, Domenico
A1 Camporesi, Sara
A1 Cleusix, Martine
A1 Conus, Philippe
A1 Crespo-Facorro, Benedicto
A1 D'Andrea, Giuseppe
A1 Demjaha, Arsime
A1 Di-Forti, Marta
A1 Do, Kim
A1 Doody, Gillian
A1 Eap, Chin B
A1 Ferchiou, Aziz
A1 Guidi, Lorenzo
A1 Homman, Lina
A1 Jenni, Raoul
A1 Joyce, Eileen
A1 Kassoumeri, Laura
A1 Lastrina, Ornella
A1 Melle, Ingrid
A1 Morgan, Craig
A1 O'Neill, Francis A
A1 Pignon, Baptiste
A1 Restellini, Romeo
A1 Richard, Jean-Romain
A1 Simonsen, Carmen
A1 Španiel, Filip
A1 Szöke, Andrei
A1 Tarricone, Ilaria
A1 Tortelli, Andrea
A1 Üçok, Alp
A1 Vazquez-Bourgon, Javier
A1 Murray, Robin M
A1 Walters, James T R
A1 Stahl, Daniel
A1 MacCabe, James H
K1 First episode psychosis
K1 Machine learning
K1 Prediction modelling
K1 Prospective longitudinal cohort
K1 Stratification
K1 Treatment resistant schizophrenia
AB 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.
PB Elsevier BV
YR 2022
FD 2022-10-12
LK http://hdl.handle.net/10668/22510
UL http://hdl.handle.net/10668/22510
LA en
NO Smart SE, Agbedjro D, Pardiñas AF, Ajnakina O, Alameda L, Andreassen OA, et al. Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium. Schizophr Res. 2022 Dec;250:1-9.
DS RISalud
RD Apr 18, 2025