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Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium
SE. Smart, D. Agbedjro, AF. Pardiñas, O. Ajnakina, L. Alameda, OA. Andreassen, TRE. Barnes, D. Berardi, S. Camporesi, M. Cleusix, P. Conus, B. Crespo-Facorro, G. D'Andrea, A. Demjaha, M. Di Forti, K. Do, G. Doody, CB. Eap, A. Ferchiou, L. Guidi,...
Schizophrenia research.
2022 ;
250 (-) :
1-9.
[pub] 20221012
Jazyk angličtina Země Nizozemsko
Typ dokumentu časopisecké články
Perzistentní odkaz
https://www.medvik.cz/link/bmc22032373
- MeSH
- antipsychotika * terapeutické užití MeSH
- lidé MeSH
- prognóza MeSH
- prospektivní studie MeSH
- psychotické poruchy * diagnóza MeSH
- stupeň vzdělání MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
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.
AP HP Hôpitaux Universitaires H Mondor DMU IMPACT FHU ADAPT Creteil France
Centre for Public Health Institute of Clinical Sciences Queens University Belfast Belfast UK
Centre for Society and Mental Health King's College London London UK
Centro de Investigacion en Red Salud Mental Sevilla Spain
Department of Medicine and Psychiatry School of Medicine University of Cantabria Santander Spain
Department of Psychiatry Hospital Universitario Virgen del Rocio IBiS Universidad de Sevilla Spain
Division of Mental Health and Addiction Oslo University Hospital Oslo Norway
Division of Psychiatry Imperial College London UK
Health Service and Population Research King's College London London UK
Istanbul University Istanbul Faculty of Medicine Department of Psychiatry Istanbul Turkey
School of Pharmaceutical Sciences University of Geneva University of Lausanne Geneva Switzerland
Citace poskytuje Crossref.org
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- $a Smart, Sophie E $u MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. Electronic address: SmartS1@cardiff.ac.uk
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- $a Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium / $c SE. Smart, D. Agbedjro, AF. Pardiñas, O. Ajnakina, L. Alameda, OA. Andreassen, TRE. Barnes, D. Berardi, S. Camporesi, M. Cleusix, P. Conus, B. Crespo-Facorro, G. D'Andrea, A. Demjaha, M. Di Forti, K. Do, G. Doody, CB. Eap, A. Ferchiou, L. Guidi, L. Homman, R. Jenni, E. Joyce, L. Kassoumeri, O. Lastrina, I. Melle, C. Morgan, FA. O'Neill, B. Pignon, R. Restellini, JR. Richard, C. Simonsen, F. Španiel, A. Szöke, I. Tarricone, A. Tortelli, A. Üçok, J. Vázquez-Bourgon, RM. Murray, JTR. Walters, D. Stahl, JH. MacCabe
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- $a 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.
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