多中心随机对照试验设计：一项针对高风险精神科住院患者出院后预防自杀的干预措施——退伍军人协调社区护理研究。
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.
机构信息
Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island, USA.
Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, New York, USA.
出版信息
Int J Methods Psychiatr Res. 2024 Dec;33(4):e70003. doi: 10.1002/mpr.70003.
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.
CLINICAL TRIALS REGISTRATION
ClinicalTrials.Gov identifier: NCT05272176 (https://www.
CLINICALTRIALS
gov/ct2/show/NCT05272176).
背景
精神科医院出院后是自杀相关行为（SRBs）风险升高的时期。尽管出院后的临床随访可能对预防 SRBs 有效，但如果针对高风险患者，那么它将更具成本效益。为此，开发了一种机器学习模型，以预测退伍军人健康管理局（VHA）精神科住院患者出院后的自杀事件，并针对高危人群进行预防干预。
方法
退伍军人协调社区护理（3C）研究是一项多中心随机对照试验，该模型用于识别高风险的 VHA 精神科住院患者（n=850），并按照相等的分配随机分为两种治疗方式：CLASP（应对长期积极自杀计划）或 TAU（常规治疗）。主要结局是在 6 个月的随访中出现 SRBs。我们将根据失访情况调整平均治疗效果，并调查治疗效果的异质性。
结果
招募工作正在进行中，将于 2024 年 9 月结束。6 个月的随访将于 2025 年夏季结束，并开始分析。
结论
研究结果将提供有关 CLASP 与 TAU 降低出院后 SRBs 的效果的信息，并为 VHA 临床医生和政策制定者提供有关在出院后几个月内针对高危精神科住院患者使用 CLASP 的靶向使用的影响的指导。
临床试验注册
ClinicalTrials.Gov 标识符：NCT05272176（https://www.clinicaltrials.gov/ct2/show/NCT05272176）。
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