Overdose Prevention Strategies for Substance Use Disorders

(FORTRESS Trial)

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: Indiana University

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

400 Participants Needed

Trial Summary

What is the purpose of this trial?

This project seeks to address the overdose epidemic by working with overdose fatality review (OFR) teams. Current OFR practices rely on a case review model where OFR teams assess one or two overdose cases to make policy and program recommendations. However, the continued rise in overdose rates and number of preventable overdose deaths suggest a need to shift OFR teams away from case review and toward using timely population-level data to better inform their recommendations and actions. The goal of this project, Fatal Overdose Review Teams - Research to Enhance Surveillance Systems (FORTRESS), is to improve standard OFR practices by equipping OFRs with a data dashboard built on near real-time aggregate data, linked across multiple sources and presented in a way that helps identify common "overdose touchpoints," or opportunities to connect individuals at risk for overdose with evidence-based treatment. During the first project phase, the FORTRESS team will design the "Overdose Touchpoints Dashboard'' (Aim 1). The FORTRESS team will also train OFR team members in "Data-Driven Decision Making" (DDDM) to effectively use the dashboard. The FORTRESS team also includes individuals involved in developing the CDC's OFR best practice guidelines and a pilot study of OFR adherence to these guidelines, which will inform the FORTRESS team's development of an "OFR Fidelity Tool'' (Aim 2). This tool will be the first of its kind. For the second project phase, the FORTRESS team will conduct a cluster-randomized stepped-wedge trial comparing the impact of the intervention (dashboard + DDDM training) versus standard OFR practices on both implementation (Aim 3) and effectiveness outcomes (Aim 4). Implementation outcomes include implementation process fidelity (Stages of Implementation Completion), staff acceptance of harm reduction philosophies (qualitative interviews), OFR fidelity to CDC best practices (FORTRESS OFR Fidelity Tool), and usability of the Overdose Touchpoint Dashboard, (Systems Usability Scale). A statewide OFR data repository serves as a rich source of data on effectiveness outcomes, including OFR team recommendation quality and local actions to implement recommended overdose prevention strategies. The FORTRESS team will also survey OFR team members to assess changes in their attitudes toward evidence-based overdose prevention strategies. In sum, the FORTRESS team is uniquely qualified to help OFRs use more comprehensive available data to inform quality, action-oriented recommendations to reduce overdose. Funding for this project comes from the HEAL Initiative (https://heal.nih.gov/).

Do I need to stop my current medications for this trial?

The trial protocol does not specify whether participants need to stop taking their current medications.

What data supports the effectiveness of the treatment Data-Driven Decision Making (DDDM) for overdose prevention in substance use disorders?

Research shows that using data and predictive models can help identify individuals at high risk of opioid overdose, allowing for targeted interventions. This approach has been effective in predicting overdose risk and could improve treatment decisions and outcomes for people with substance use disorders. ^{1 2 3 4 5}

Is the Data-Driven Decision Making (DDDM) approach for overdose prevention safe for humans?

The research does not provide specific safety data for the Data-Driven Decision Making (DDDM) approach in humans, but it discusses the use of predictive models and big data to assess and reduce overdose risk, which suggests a focus on improving safety in treatment strategies. ^{1 2 3 4 6}

How does this treatment differ from other overdose prevention strategies?

This treatment is unique because it uses big data and predictive modeling to estimate overdose risk and tailor interventions for individuals with opioid use disorder. Unlike traditional methods, it leverages advanced analytics to provide targeted treatment recommendations, potentially improving outcomes by predicting treatment responses and reducing overdose risks. ^{2 4 7 8 9}

Research Team

Matthew C Aalsma, PhD

Principal Investigator

Indiana University School of Medicine

Eligibility Criteria

This trial is for members of local overdose fatality review (OFR) teams or leaders in Indiana organizations involved with OFR, such as public jail administrators and health directors. It also includes residents of Indiana who have had fatal or non-fatal overdoses.

Inclusion Criteria

FORTRESS staff personnel recruited to complete surveys, focus groups and/or interviews must be:

Residents of Indiana that have experienced fatal and/or non-fatal overdose as identified by administrative data sources

OR 2) local county leader of organizations represented by OFR (OFR facilitator, public jail administrators, chief of police, judge, addiction treatment CEO/CFO, public health director, etc.)

Exclusion Criteria

N/A

Timeline

Design and Training

The FORTRESS team designs the 'Overdose Touchpoints Dashboard' and trains OFR team members in 'Data-Driven Decision Making' (DDDM).

14 months

Cluster-randomized Stepped-wedge Trial

Conduct a trial comparing the impact of the intervention (dashboard + DDDM training) versus standard OFR practices on implementation and effectiveness outcomes.

40 months

Follow-up

Participants are monitored for changes in attitudes toward evidence-based overdose prevention strategies and the quality of OFR recommendations.

5 years

Treatment Details

Interventions

• Data-Driven Decision Making (DDDM)

Trial Overview The FORTRESS project aims to improve how OFR teams work by giving them a data dashboard and training in Data-Driven Decision Making (DDDM). The study will compare these new tools against standard practices to see if they can better prevent overdoses.

Participant Groups

Group I: FORTRESS Experimental Treatment 2 Interventions

Participating counties receive both training in data-driven decision making and inventory of overdose-prevention strategies

Group II: OFR Team Practice as Usual Active Control 1 Intervention

Data are collected regarding standard OFR Team practice and outcomes before implementation of the FORTRESS Intervention

Who Is Running the Clinical Trial?

Indiana University

Lead Sponsor

Trials

1,063

Recruited

1,182,000+

National Institute on Drug Abuse (NIDA)

Collaborator

Trials

2,658

Recruited

3,409,000+

Findings from Research

In 2022, the US faced over 80,000 opioid overdose deaths, highlighting the severe impact of opioid use disorder (OUD) on public health and society.

The article emphasizes the importance of using analytics and modeling to inform policies aimed at understanding and addressing the opioid epidemic, including prevention, treatment, and potential reforms in regulation and criminal justice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Responding to the US opioid crisis: leveraging analytics to support decision making. Brandeau, ML.[2023]

References

1. Ireland pubmed.ncbi.nlm.nih.gov

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients. [2023]

2. England pubmed.ncbi.nlm.nih.gov

Big data and predictive modelling for the opioid crisis: existing research and future potential. [2023]

3. United States pubmed.ncbi.nlm.nih.gov

Heroin and healthcare: patient characteristics and healthcare prior to overdose. [2021]

4. United States pubmed.ncbi.nlm.nih.gov

Assessing opioid overdose risk: a review of clinical prediction models utilizing patient-level data. [2022]

5. Ireland pubmed.ncbi.nlm.nih.gov

Development and validation of an overdose risk prediction tool using prescription drug monitoring program data. [2023]

6. Switzerland pubmed.ncbi.nlm.nih.gov

Suicidality as a Predictor of Overdose among Patients with Substance Use Disorders. [2022]

7. Australia pubmed.ncbi.nlm.nih.gov

Addressing the overdose epidemic requires timely access to data to guide interventions. [2018]

8. Ireland pubmed.ncbi.nlm.nih.gov

Evidence for state, community and systems-level prevention strategies to address the opioid crisis. [2022]

9. Netherlands pubmed.ncbi.nlm.nih.gov

Responding to the US opioid crisis: leveraging analytics to support decision making. [2023]