Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium
Jazyk angličtina Země Nizozemsko Médium print-electronic
Typ dokumentu časopisecké články
Grantová podpora
MR/L010305/1
Medical Research Council - United Kingdom
MR/L011794/1
Medical Research Council - United Kingdom
PubMed
36242784
PubMed Central
PMC9834064
DOI
10.1016/j.schres.2022.09.009
PII: S0920-9964(22)00342-5
Knihovny.cz E-zdroje
- Klíčová slova
- First episode psychosis, Machine learning, Prediction modelling, Prospective longitudinal cohort, Stratification, Treatment resistant schizophrenia,
- 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
- Názvy látek
- antipsychotika * 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.
Centre for Public Health Institute of Clinical Sciences Queens University Belfast Belfast UK
Division of Psychiatry Imperial College London UK
Istanbul University Istanbul Faculty of Medicine Department of Psychiatry Istanbul Turkey
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