Pub Date : 2025-12-02DOI: 10.1016/s2215-0366(25)00357-8
Mu’taz M Al Shorman, Ananth Nazarene, Amal S Alobaidli, Abdulqadir J Nashwan
No Abstract
没有抽象的
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Pub Date : 2025-11-26DOI: 10.1016/s2215-0366(25)00328-1
Danella M Hafeman
No Abstract
没有抽象的
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Pub Date : 2025-11-26DOI: 10.1016/s2215-0366(25)00307-4
Maite Arribas, Andrea de Micheli, Kamil Krakowski, Daniel Stahl, Christoph U Correll, Allan H Young, Ole A Andreassen, Eduard Vieta, Celso Arango, Philip McGuire, Dominic Oliver, Paolo Fusar-Poli
Background
Efficient detection of individuals at risk of developing psychotic disorders or bipolar disorders is a crucial step to improving mental health outcomes in young people. A novel, transdiagnostic approach to jointly detect individuals at risk for either psychotic disorders or bipolar disorders would maximise the effect of prevention. The aim of this study is to develop and validate an individualised prediction model to detect the risk of developing psychotic disorders or bipolar disorders in the UK.
Methods
This RECORD Statement and TRIPOD+AI compliant study describes the development and validation of a clinical prediction model to estimate the risk of developing psychotic or bipolar disorders using data from patients of all ages with an index diagnosis of a non-organic, non-psychotic and non-bipolar mental disorder recorded in electronic health records from South London and Maudsley (SLaM in the UK) secondary mental health care between Jan 1, 2008, and Aug 10, 2021. Exclusion criteria included receiving long-acting injectable antipsychotics or clozapine before a diagnosis of bipolar or psychotic disorders, no recorded contact with SLaM services after the index date, and an index date falling within the washout period Jan 1, 2008, to June 30, 2008. A least absolute shrinkage and selection operator-regularised (LASSO) Cox proportional hazards model was developed to estimate the 6-year risk of developing psychotic disorders or bipolar disorders, incorporating sociodemographic and clinical predictors at index date (five predictors), and medication (four predictors), hospitalisation (two predictors) and natural language processing-derived signs and symptoms and substance use (66 predictors), derived using a 6-month look-back period. Model performance was assessed using internal–external validation, sequentially leaving out one borough from the SLaM area for testing and averaging performance across all five boroughs. The final model was fit with data across all the boroughs. Performance was assessed via discrimination (C-index), calibration (calibration slope and calibration-in-the-large), and potential clinical utility (decision curve analysis) during internal–external cross-validation. Individuals with lived experience of bipolar disorders or psychotic disorders were not involved in the research or writing process.
Findings
In total, data from 127 868 patients were included. 64 980 (50·8%) of the dataset were male, 62 711 (49·0%) were female, and 89 (0·1%) were other gender. For self-assigned ethnicity, the dataset was 71 390 (55·8%) White, 18 025 (14·1%) Black, 7257 (5·7%) other, 6270 (4·9%) Asian, and 5022 (3·9%) mixed (19 904 [15·6%] were missing ethnicity data). The mean age was 33·4 years (SD 18·8 [IQR 17·9–44·9]). The cumulative risk incidence of psychotic disorders or bipolar disorders was 0·0827 (95% CI 0·0784–0·0870) within 6 years (mean follow-up 622 days [SD 687]). The model showed the following performance
{"title":"Joint detection of risk for psychotic disorders or bipolar disorders in clinical practice in the UK: development and validation of a clinical prediction model","authors":"Maite Arribas, Andrea de Micheli, Kamil Krakowski, Daniel Stahl, Christoph U Correll, Allan H Young, Ole A Andreassen, Eduard Vieta, Celso Arango, Philip McGuire, Dominic Oliver, Paolo Fusar-Poli","doi":"10.1016/s2215-0366(25)00307-4","DOIUrl":"https://doi.org/10.1016/s2215-0366(25)00307-4","url":null,"abstract":"<h3>Background</h3>Efficient detection of individuals at risk of developing psychotic disorders or bipolar disorders is a crucial step to improving mental health outcomes in young people. A novel, transdiagnostic approach to jointly detect individuals at risk for either psychotic disorders or bipolar disorders would maximise the effect of prevention. The aim of this study is to develop and validate an individualised prediction model to detect the risk of developing psychotic disorders or bipolar disorders in the UK.<h3>Methods</h3>This RECORD Statement and TRIPOD+AI compliant study describes the development and validation of a clinical prediction model to estimate the risk of developing psychotic or bipolar disorders using data from patients of all ages with an index diagnosis of a non-organic, non-psychotic and non-bipolar mental disorder recorded in electronic health records from South London and Maudsley (SLaM in the UK) secondary mental health care between Jan 1, 2008, and Aug 10, 2021. Exclusion criteria included receiving long-acting injectable antipsychotics or clozapine before a diagnosis of bipolar or psychotic disorders, no recorded contact with SLaM services after the index date, and an index date falling within the washout period Jan 1, 2008, to June 30, 2008. A least absolute shrinkage and selection operator-regularised (LASSO) Cox proportional hazards model was developed to estimate the 6-year risk of developing psychotic disorders or bipolar disorders, incorporating sociodemographic and clinical predictors at index date (five predictors), and medication (four predictors), hospitalisation (two predictors) and natural language processing-derived signs and symptoms and substance use (66 predictors), derived using a 6-month look-back period. Model performance was assessed using internal–external validation, sequentially leaving out one borough from the SLaM area for testing and averaging performance across all five boroughs. The final model was fit with data across all the boroughs. Performance was assessed via discrimination (C-index), calibration (calibration slope and calibration-in-the-large), and potential clinical utility (decision curve analysis) during internal–external cross-validation. Individuals with lived experience of bipolar disorders or psychotic disorders were not involved in the research or writing process.<h3>Findings</h3>In total, data from 127 868 patients were included. 64 980 (50·8%) of the dataset were male, 62 711 (49·0%) were female, and 89 (0·1%) were other gender. For self-assigned ethnicity, the dataset was 71 390 (55·8%) White, 18 025 (14·1%) Black, 7257 (5·7%) other, 6270 (4·9%) Asian, and 5022 (3·9%) mixed (19 904 [15·6%] were missing ethnicity data). The mean age was 33·4 years (SD 18·8 [IQR 17·9–44·9]). The cumulative risk incidence of psychotic disorders or bipolar disorders was 0·0827 (95% CI 0·0784–0·0870) within 6 years (mean follow-up 622 days [SD 687]). The model showed the following performance ","PeriodicalId":48784,"journal":{"name":"Lancet Psychiatry","volume":"4 1","pages":""},"PeriodicalIF":64.3,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145608967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-11DOI: 10.1016/s2215-0366(25)00273-1
Melissa P DelBello, Jeffrey A Welge, Christina C Klein, Thomas J Blom, Victor Fornari, Claudine Higdon, Michael T Sorter, Brian Kurtz, Cindy Starr, Andrew Smith, Bin Huang, Chen Chen, Avani C Modi, Nancy Crimmins, Christoph U Correll
<h3>Background</h3>Second-generation antipsychotics are widely used to treat patients with bipolar spectrum disorders and effectively manage mood symptoms but can often cause substantial weight gain and other metabolic alterations that elevate long-term risks of cardiovascular disease and premature mortality. Metformin has been shown to be safe and efficacious for ameliorating weight gain but has not been evaluated in typical clinical settings or for more than 6 months in this population and is not widely used as standard of care. Therefore, we conducted a pragmatic clinical trial to assess the effect of metformin treatment in young people treated with second-generation antipsychotics who had a bipolar spectrum disorder along with overweight or obesity.<h3>Methods</h3>In this multi-site, open-label, pragmatic parallel group study, we enrolled overweight or obese youth aged 8–19 years, previously or currently diagnosed with a bipolar spectrum disorder, and treated with or starting a second-generation antipsychotic. Participants were recruited and followed at 64 clinical sites (community-based mental health centres or academic health centres) in the USA. Sites were eligible for participation if they projected an enrolment rate of at least three patients per month and a minimum total number of 90 patients. Participants were randomly assigned (1:1) to the healthy eating and physical activity (LIFE) or the metformin plus LIFE (MET plus LIFE) interventions, within eight strata defined by baseline BMI percentile (overweight [85th to <95th] <em>vs</em> obese [≥95th]); second-generation antipsychotic-naive (starting <em>vs</em> continuing a second-generation antipsychotic at baseline); and sex assigned at birth, and using block randomisation (blocks of six). The co-primary outcomes were change in age-normalised and sex-normalised BMI Z-score at 6 months and 24 months in the intention-to-treat population. People with lived experience with bipolar disorders were involved in the design, conduct, and reporting of this trial. The Patient-Centered Outcomes Research Institute identification was PCS-1406-19276 and the study was registered at ClinicalTrials.gov, NCT02515773, and is completed.<h3>Findings</h3>Between Nov 5, 2015, and Feb 10, 2022, 1633 individuals provided consent for study inclusion, 68 were excluded (26 withdrew before baseline assessments and 42 did not pass screening assessments), and 1565 were randomly assigned (777 assigned to the MET plus LIFE group and 788 assigned to the LIFE group). Data were available from 1252 participants at month 6 (565 in the MET plus LIFE group and 687 in the LIFE group), and 1299 participants at month 24 (579 in the MET plus LIFE group and 720 in the LIFE group). 829 (53%) participants were male and 736 (47%) were female. The mean age of participants was 13·9 years (SD 2·9). 1023 (65%) were White or Caucasian and 290 (19%) were Black or African American. After 6 months and 24 months, assignment to the MET plus L
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