Lauren M. Weinstock, Todd M. Bishop, Mark S. Bauer, Jeffrey Benware, Robert M. Bossarte, John Bradley, Steven K. Dobscha, Jessica Gibbs, Sarah M. Gildea, Hannah Graves, Gretchen Haas, Samuel House, Chris J. Kennedy, Sara J. Landes, Howard Liu, Alex Luedtke, Brian P. Marx, Aletha Miller, Matthew K. Nock, Richard R. Owen, Wilfred R. Pigeon, Nancy A. Sampson, Alejandro Santiago-Colon, Geetha Shivakumar, Snezana Urosevic, Ronald C. Kessler
{"title":"针对高风险精神病住院患者的出院后自杀预防干预多中心随机对照试验的设计：退伍军人社区协调护理研究》。","authors":"Lauren M. Weinstock, Todd M. Bishop, Mark S. Bauer, Jeffrey Benware, Robert M. Bossarte, John Bradley, Steven K. Dobscha, Jessica Gibbs, Sarah M. Gildea, Hannah Graves, Gretchen Haas, Samuel House, Chris J. Kennedy, Sara J. Landes, Howard Liu, Alex Luedtke, Brian P. Marx, Aletha Miller, Matthew K. Nock, Richard R. Owen, Wilfred R. Pigeon, Nancy A. Sampson, Alejandro Santiago-Colon, Geetha Shivakumar, Snezana Urosevic, Ronald C. Kessler","doi":"10.1002/mpr.70003","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (<i>n</i> = 850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge.</p>\n </section>\n \n <section>\n \n <h3> Clinical trials registration</h3>\n \n <p>ClinicalTrials.Gov identifier: NCT05272176 (https://www.clinicaltrials.gov/ct2/show/NCT05272176).</p>\n </section>\n </div>","PeriodicalId":50310,"journal":{"name":"International Journal of Methods in Psychiatric Research","volume":"33 4","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443605/pdf/","citationCount":"0","resultStr":"{\"title\":\"Design of a multicenter randomized controlled trial of a post-discharge suicide prevention intervention for high-risk psychiatric inpatients: The Veterans Coordinated Community Care Study\",\"authors\":\"Lauren M. 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To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (<i>n</i> = 850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Recruitment is underway and will end September 2024. 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Design of a multicenter randomized controlled trial of a post-discharge suicide prevention intervention for high-risk psychiatric inpatients: The Veterans Coordinated Community Care Study
Background
The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention.
Methods
The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (n = 850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects.
Results
Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025.
Conclusion
Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge.
期刊介绍：
The International Journal of Methods in Psychiatric Research (MPR) publishes high-standard original research of a technical, methodological, experimental and clinical nature, contributing to the theory, methodology, practice and evaluation of mental and behavioural disorders. The journal targets in particular detailed methodological and design papers from major national and international multicentre studies. There is a close working relationship with the US National Institute of Mental Health, the World Health Organisation (WHO) Diagnostic Instruments Committees, as well as several other European and international organisations.
MPR aims to publish rapidly articles of highest methodological quality in such areas as epidemiology, biostatistics, generics, psychopharmacology, psychology and the neurosciences. Articles informing about innovative and critical methodological, statistical and clinical issues, including nosology, can be submitted as regular papers and brief reports. Reviews are only occasionally accepted.
MPR seeks to monitor, discuss, influence and improve the standards of mental health and behavioral neuroscience research by providing a platform for rapid publication of outstanding contributions. As a quarterly journal MPR is a major source of information and ideas and is an important medium for students, clinicians and researchers in psychiatry, clinical psychology, epidemiology and the allied disciplines in the mental health field.
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