{"title":"Predicting treatment resistance in positive and negative symptom domains from first episode psychosis: Development of a clinical prediction model","authors":"","doi":"10.1016/j.schres.2024.09.010","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP).</p></div><div><h3>Methods</h3><p>Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, <em>n</em> = 51 (4.97 %) participants were treatment resistant in the positive domain and <em>n</em> = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures.</p></div><div><h3>Results</h3><p>The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56).</p></div><div><h3>Conclusions</h3><p>Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.</p></div>","PeriodicalId":21417,"journal":{"name":"Schizophrenia Research","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0920996424004213/pdfft?md5=efb421067df4b03b9914361a7ac792ca&pid=1-s2.0-S0920996424004213-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Schizophrenia Research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0920996424004213","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Treatment resistance (TR) in schizophrenia may be defined by the persistence of positive and/or negative symptoms despite adequate treatment. Whilst previous investigations have focused on positive symptoms, negative symptoms are highly prevalent, impactful, and difficult to treat. In the current study we aimed to develop easily employable prediction models to predict TR in positive and negative symptom domains from first episode psychosis (FEP).
Methods
Longitudinal cohort data from 1027 individuals with FEP was utilised. Using a robust definition of TR, n = 51 (4.97 %) participants were treatment resistant in the positive domain and n = 56 (5.46 %) treatment resistant in the negative domain 12 months after first presentation. 20 predictor variables, selected by existing evidence and availability in clinical practice, were entered into two LASSO regression models. We estimated the models using repeated nested cross-validation (NCV) and assessed performance using discrimination and calibration measures.
Results
The prediction model for TR in the positive domain showed good discrimination (AUC = 0.72). Twelve predictor variables (male gender, cannabis use, age, positive symptom severity, depression and academic and social functioning) were retained by each outer fold of the NCV procedure, indicating importance in prediction of the outcome. However, our negative domain model failed to discriminate those with and without TR, with results only just over chance (AUC = 0.56).
Conclusions
Treatment resistance of positive symptoms can be accurately predicted from FEP using routinely collected baseline data, however prediction of negative domain-TR remains a challenge. Detailed negative symptom domains, clinical data, and biomarkers should be considered in future longitudinal studies.
期刊介绍:
As official journal of the Schizophrenia International Research Society (SIRS) Schizophrenia Research is THE journal of choice for international researchers and clinicians to share their work with the global schizophrenia research community. More than 6000 institutes have online or print (or both) access to this journal - the largest specialist journal in the field, with the largest readership!
Schizophrenia Research''s time to first decision is as fast as 6 weeks and its publishing speed is as fast as 4 weeks until online publication (corrected proof/Article in Press) after acceptance and 14 weeks from acceptance until publication in a printed issue.
The journal publishes novel papers that really contribute to understanding the biology and treatment of schizophrenic disorders; Schizophrenia Research brings together biological, clinical and psychological research in order to stimulate the synthesis of findings from all disciplines involved in improving patient outcomes in schizophrenia.