Article
Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium
Author/s | Smart, Sophie E.
Agbedjro, Deborah Pardiñas, Antonio F. Ajnakina, Olesya Alameda, Luis Andreassen, Ole A. Crespo Facorro, Benedicto MacCabe, James H. |
Department | Universidad de Sevilla. Departamento de Psiquiatría |
Publication Date | 2022-10-12 |
Deposit Date | 2023-06-12 |
Published in |
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Abstract | Introduction
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 ... Introduction 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. Methods 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. Results 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 %). Implications 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. |
Funding agencies | Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South London at King's College Hospital National Health Service Foundation Trust European Community (EC) European Community's Seventh Framework Program European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) Fundación Marqués de Valdecilla Institute of Psychiatry, Psychology, and Neuroscience at King's College London; Psychiatry Research Trust Fondation Alamaya Instituto de Salud Carlos III King's College London Maudsley Charity Research Fund. Medical Research Council Ministerio de Economia, Industria y Competitividad (MINECO). España Ministry of Health of the Czech Republic National Center of Competence in Research (NCCR) "SYNAPSY - The Synaptic Bases of Mental Diseases" from the Swiss National Science Foundation National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust Plan Nacional de Drogas. Beca de investigación Research and Development Office of Northern Ireland Research Council of Norway SENY Fundatio Research Grant South-Eastern Norway Regional Health Authority Swiss National Science Foundation (SNFS) UK Medical Research Council UK National Institute of Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health, South London and Maudsley NHS Mental Health Foundation Trust (SLaM) Wellcome Trust |
Project ID. | HEALTH-F2-2010-241909
HEALTH-F2-2009-241909 HEALTH-F2-2010-241909 SAF2016-76046-R SAF2013-46292-R FIS 00/3095 PI020499 PI050427 PI060507 MR/L011794/1 SAF2016-76046-R SAF2013-46292-R NU20-04-00393 51AU40_125759 2005-Orden sco/3246/2004 223273/F50 300309 283798 CI 2005-0308007 2006233 2006258 2011085 2014102 2015088 2017-112 320030_135736/1 320030120686 324730-144064 320030-173211 171804 G0500817 042025 052247 064607 |
Citation | 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. https://doi.org/10.1016/j.schres.2022.09.009. |
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