Health Care Processes Contributing to Suicide Risk in Veterans During and After Residential Substance Abuse Treatment

Pilot randomized controlled trial of a brief strategy to prevent suicide after discharge from residential addiction treatment

INTRODUCTION

Veterans are at greater risk for suicide and veterans with substance use disorder (SUD) have an even greater risk. Little research has looked into brief interventions to prevent suicide in this population in residential substance use treatment programs.

METHOD

We conducted a pilot, randomized controlled trial of a brief suicide prevention strategy called Veterans Affairs Brief Intervention and Contact Program (VA BIC) in patients participating in the Residential Recovery Center (RRC) SUD 28-day program and deemed at risk for suicide. We measured changes in symptoms at 1-, 3-, and 6-months. We looked at social connectedness, suicidal ideation, hopelessness, thwarted belongingness, perceived burdensomeness, and treatment engagement.

RESULTS

The study enrolled twenty patients. One participant withdrew immediately after baseline. We found that adherence to VA BIC components was high, as 100 % of patients (N = 10) completed 70 % or more of the VA BIC visits. Furthermore, 80 % of intervention group patients (N = 8) completed all VA BIC components. During the six-month follow-up, suicidal ideation improved in patients assigned to VA BIC, while it worsened in the standard care arm. Similarly, patients assigned to VA BIC reported a reduction in perceived burdensomeness over the six-month follow-up period while it worsened in the standard care arm. Additionally, VA BIC may modestly improve treatment engagement in the first month postdischarge.

CONCLUSION

We were able to recruit and enroll patients from a residential SUD treatment program into a clinical trial of the VA BIC intervention. Our preliminary results suggest that VA BIC may be useful in reducing suicidal ideation and perceived burdensomeness in patients who are discharged from residential SUD treatment programs and increasing treatment engagement. Future trials of VA BIC should determine whether VA BIC can reduce the risk of suicide in patients who are discharged from residential SUD treatment programs.

Predictors of Suicidal Ideation During Residential Substance Use Treatment

Background: Individuals with substance use disorders (SUDs) and co-occurring chronic health and/or psychiatric conditions face unique challenges in treatment and may be at a greater risk for suicidal ideation relative to persons with SUD alone.

Methods: In a sample of individuals entering residential SUD treatment in 2019 and 2020 (N = 10,242), we tested adjusted and unadjusted associations between suicidal ideation and (1) psychiatric symptoms and (2) chronic health conditions at treatment intake and during treatment using logistic and generalized logistic models.

Results: Over a third of the sample endorsed suicidal ideation at intake, though the prevalence of suicidal ideation decreased during treatment. In both adjusted and unadjusted models, individuals who reported past-month self-harm, those who reported a lifetime suicide attempt, and individuals who screened positive for co-occurring anxiety, depression, and/or posttraumatic stress disorder were at elevated risk of endorsing suicidal ideation at intake and during treatment (P values < .001). In unadjusted models, chronic pain (odds ratio [OR] = 1.51, P < .001) and hepatitis C virus (OR = 1.65, P < .001) were associated with an elevated risk for suicidal ideation at intake, and chronic pain was associated with elevated risk for suicidal ideation during treatment (OR = 1.59, P < .001).

Conclusions: Increasing accessibility to integrated treatments (ie, those that address psychiatric and chronic health conditions) for patients experiencing suicidal ideation may be beneficial in residential SUD treatment settings. Developing predictive models to identify those most at risk of suicidal ideation in real time remains a relevant direction for future work.

Leveraging unstructured electronic medical record notes to derive population-specific suicide risk models

Electronic medical record (EMR)-based suicide risk prediction methods typically rely on analysis of structured variables such as demographics, visit history, and prescription data. Leveraging unstructured EMR notes may improve predictive accuracy by allowing access to nuanced clinical information. We utilized natural language processing (NLP) to analyze a large EMR note corpus to develop a data-driven suicide risk prediction model. We developed a matched case-control sample of U.S. Department of Veterans Affairs (VA) patients in 2015 and 2016. We randomly matched each case (all patients that died by suicide in that interval, n = 5029) with five controls (patients that remained alive). We processed note corpus using NLP methods and applied machine-learning classification algorithms to output. We calculated area under the curve (AUC) and risk tiers to determine predictive accuracy. NLP-derived models demonstrated strong predictive accuracy. Patients that scored within top 10% of risk model accounted for up to 29% of suicide decedents. NLP-derived model compares positively to other leading prediction methods. Our approach is highly implementable, only requiring access to text data and open-source software. Additional studies should evaluate ensemble models incorporating NLP-derived information alongside more typical structured variables.

Association between suicide, external-cause and all-cause mortality and irregular mental health discharge among the US veteran population

Background

Irregular hospital discharge is highly prevalent among people admitted to hospital for mental health reasons. No study has examined the relationship between irregular discharge, post-discharge mortality and treatment setting (i.e. mortality after patients are discharged from acute in-patient or residential mental health settings).

Aims

To understand the relationship between irregular discharge and mortality among patients discharged from acute in-patient and residential settings.

Method

A retrospective study was conducted in members of the US veteran population discharged from acute in-patient or residential settings of the US Department of Veterans Affairs between 2003 and 2018. Multivariate Cox proportional hazards were used to evaluate associations between irregular discharge and suicide, external-cause (as defined by ICD-10 Codes: V01-Y98) and all-cause mortality in the first 30-, 90- and 180-days post-discharge.

Results

There were over 1.5 million mental health discharges between 2003 and 2018. Patients with an irregular discharge were at increased risk for suicide, external-cause and all-cause mortality in the first 180 days after discharge. In the first 30 days after discharge, patients with irregular discharge had more than three times greater suicide risk than patients with regular discharge (adjusted hazard ratio (HR) = 3.41, 95% CI 2.21–5.25). Suicide risk was higher among patients with irregular discharge in the first 30 days after acute in-patient discharge (adjusted HR = 1.55, 95% CI 1.11–2.16). In both settings, the mortality risk associated with irregular discharge attenuated but remained elevated within 90 and 180 days.

Conclusions

Irregular discharge after an acute in-patient or residential stay poses a large risk for mortality soon after discharge. Clinicians must identify effective interventions to mitigate harms associated with irregular discharge in these settings.