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).
Keywords: behavior; clinical trials; suicide.
© 2024 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.