期刊
SCHIZOPHRENIA RESEARCH
卷 250, 期 -, 页码 1-9出版社
ELSEVIER
DOI: 10.1016/j.schres.2022.09.009
关键词
Treatment resistant schizophrenia; First episode psychosis; Prospective longitudinal cohort; Prediction modelling; Stratification; Machine learning
类别
资金
- Medical Research Council [MR/L011794/1]
- National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust
- King's College London
- Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South London at King's College Hospital National Health Service Foundation Trust
- UK Medical Research Council [G0500817]
- Research and Development Office of Northern Ireland
- European Community's Seventh Framework Program [HEALTH-F2-2010-241909]
- UK National Institute of Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health, South London and Maudsley NHS Mental Health Foundation Trust (SLaM)
- Institute of Psychiatry, Psychology, and Neuroscience at King's College London
- Psychiatry Research Trust
- Maudsley Charity Research Fund
- European Community [HEALTH-F2-2010-241909, HEALTH-F2-2009-241909]
- Swiss National Science Foundation [320030_135736/1, 320030120686, 324730-144064, 320030-173211, 171804]
- National Center of Competence in Research (NCCR) SYNAPSY - The Synaptic Bases of Mental Diseases from the Swiss National Science Foundation [51AU40_125759]
- Fondation Alamaya
- Research Council of Norway [223273/F50, 300309, 283798]
- South-Eastern Norway Regional Health Authority [2006233, 2006258, 2011085, 2014102, 2015088, 2017-112]
- Ministry of Health of the Czech Republic [NU20-04-00393]
- Instituto de Salud Carlos III [FIS 00/3095, PI020499, PI050427, PI060507]
- Plan Nacional de Drogas Research Grant [2005-Orden sco/3246/2004]
- SENY Fundatio Research Grant [CI 2005-0308007]
- Fundacion Marques de Valdecilla [A/02/07, API07/011]
- MINECO [SAF2016-76046-R, SAF2013-46292-R]
- FEDER [SAF2016-76046-R, SAF2013-46292-R]
- Wellcome Trust [042025, 052247, 064607]
This study aimed to identify characteristics at the first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and develop a parsimonious prediction model for TR. The findings suggest that younger age of psychosis onset and fewer years in education are independently associated with an increased risk of developing TR.
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 cat-egorised 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据