Decision Support Tool for Atrial Fibrillation

Overseen By Larry Jackson, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Duke University

Must be taking: Oral anticoagulants

Disqualifiers: Non-English speakers, others

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

Compensation: Varies

Number of Visits: Unknown

Duration: 12 months

40 Participants Needed

Trial Summary

What is the purpose of this trial?

The study is a pilot analysis using a decision on the risk and benefits of oral anticoagulation for stroke reduction for patients with non-valvular atrial fibrillation. This study is a feasibility and acceptability analysis but will also measure preliminary effectiveness measures. The investigator hypothesizes that a patient decision support tool will increase decision quality and secondarily increase the use of oral anticoagulation in Black patients with non-valvular atrial fibrillation.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It focuses on starting oral anticoagulation for stroke prevention in patients with non-valvular atrial fibrillation.

What data supports the effectiveness of the Patient Decision Support Tool treatment for atrial fibrillation?

Research shows that decision support tools can improve patient satisfaction and engagement in treatment plans for atrial fibrillation, as seen in the ENHANCE-AF trial, which evaluated a decision aid for stroke prevention in 1,200 participants. ^{1 2 3 4 5}

Is the Decision Support Tool for Atrial Fibrillation safe for humans?

The available research does not provide specific safety data for the Decision Support Tool for Atrial Fibrillation, but it highlights the importance of patient-reported outcomes in understanding the safety of treatments in general. This approach helps ensure that patient experiences and perspectives are considered in safety assessments. ^{6 7 8 9 10}

How is the Decision Support Tool for Atrial Fibrillation different from other treatments?

The Decision Support Tool for Atrial Fibrillation is unique because it is an interactive decision aid designed to help patients make informed choices about their treatment options by aligning with their personal preferences and goals. Unlike traditional treatments, this tool focuses on enhancing shared decision-making between patients and healthcare providers, particularly for rhythm and symptom management in atrial fibrillation. ^{2 11 12 13 14}

Research Team

Larry Jackson

Principal Investigator

Duke University

Eligibility Criteria

This trial is for Black and White adults over 18 with non-valvular atrial fibrillation (NVAF) who have a CHA2DS2-VASc score of at least 2, indicating a higher risk of stroke. They must be new patients at primary care or cardiology clinics considering starting oral anticoagulation but cannot participate if they can't speak English or provide informed consent.

Inclusion Criteria

I am starting oral anticoagulation treatment at a primary care or cardiology clinic.

I have been diagnosed with non-valvular atrial fibrillation.

You have a score of 2 or more on the CHA2DS2-VASc scale.

Exclusion Criteria

I am unable to understand or sign the consent form.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants use a patient decision support tool in conjunction with physician counseling to discuss the risk and benefits of systemic oral anticoagulation

12 months

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

Treatment Details

Interventions

• Patient Decision Support Tool

Trial Overview The study tests whether a patient decision support tool can improve the quality of decisions made by patients regarding the use of oral anticoagulants to prevent strokes in NVAF. It aims to see if this tool helps, especially among Black patients, and will assess its feasibility and acceptability.

Participant Groups

Group I: Patient Decision Support Tool Experimental Treatment 1 Intervention

Patient decision support tool to be used in conjunction with physician counseling to discuss the risk and benefits of systemic oral anticoagulation.

Group II: Usual Healthcare Counseling Active Control 1 Intervention

Traditional physician counseling regarding the risk and benefits of systemic oral anticoagulation

Who Is Running the Clinical Trial?

Duke University

Lead Sponsor

Trials

2,495

Recruited

5,912,000+

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

Findings from Research

The ENHANCE-AF trial is a multicenter, randomized controlled study involving 1,200 participants aimed at evaluating a new decision aid for shared decision making (SDM) in anticoagulation therapy for patients with atrial fibrillation (AF).

The primary outcome is to assess the Decisional Conflict Scale one month after the decision-making visit, with secondary outcomes including patient and clinician satisfaction, adherence to anticoagulation, and rates of major bleeding, death, stroke, or transient ischemic attack over a 6-month follow-up.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The ENHANCE-AF clinical trial to evaluate an atrial fibrillation shared decision-making pathway: Rationale and study design. Baykaner, T., Pundi, K., Lin, B., et al.[2022]

A review identified 14 shared decision-making (SDM) tools for patients with atrial fibrillation (AF) considering stroke prevention, most of which provided current information but lacked practical considerations like cost.

While five of these tools showed small improvements in patient knowledge and reduced decisional conflict, their effectiveness in promoting high-quality SDM remains uncertain, highlighting the need for better user-centered development and evaluation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Shared Decision Making Tools for People Facing Stroke Prevention Strategies in Atrial Fibrillation: A Systematic Review and Environmental Scan. Torres Roldan, VD., Brand-McCarthy, SR., Ponce, OJ., et al.[2021]

The PROSPER Consortium aims to enhance safety reporting in healthcare by integrating patient-reported outcomes (PROs) into the assessment of adverse events, recognizing the importance of the patient's perspective in understanding treatment effects.

Despite advancements in technology and increased focus on patient involvement, current safety reporting still heavily relies on healthcare professionals, which can lead to under-reporting and discrepancies between patient experiences and clinician perceptions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-Reported Outcome Measures in Safety Event Reporting: PROSPER Consortium guidance. Banerjee, AK., Okun, S., Edwards, IR., et al.[2022]

From 2011 to 2015, an estimated 145,490 emergency department visits annually in the U.S. were due to adverse events from antibiotics, highlighting a significant safety concern.

Allergic reactions were the leading cause of these visits, accounting for 74.3% of cases, with younger adults (ages 20-34) experiencing the highest rates of antibiotic-related adverse events compared to older adults.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

National Estimates of Emergency Department Visits for Antibiotic Adverse Events Among Adults-United States, 2011-2015. Geller, AI., Lovegrove, MC., Shehab, N., et al.[2020]

Patient support programs (PSPs) were the only data source to contribute to a drug safety label change, indicating their potential value in monitoring safety, while market research programs and social media did not lead to any label changes.

Overall, the study found that traditional PSPs provided more reliable safety information compared to market research programs and social media, which had low completeness scores, suggesting that not all data sources are equally effective in enhancing the understanding of a product's safety profile.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Industry Assessment of the Contribution of Patient Support Programs, Market Research Programs, and Social Media to Patient Safety. Jokinen, J., Bertin, D., Donzanti, B., et al.[2020]

A proof-of-concept study using recurrent neural networks (RNNs) showed promise for automating the classification of adverse event (AE) terminology from FDA's FAERS and structured product labels (SPLs), which could improve regulatory efficiency.

The model performed well on SPL data, matching or exceeding conventional methods, but had mixed results on FAERS data, suggesting that more extensive data sets are needed for better generalization and validation of AE detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets. Knisely, BM., Hatim, Q., Vaughn-Cooke, M.[2022]

The study highlights the inefficiency of manual management of serious adverse events (AEs) and reactions (ARs) in clinical trials, emphasizing the need for improved reporting systems.

A new data model has been developed to facilitate automatic code generation for a software tool designed to assist in the management of AE/AR data, potentially enhancing the efficiency and effectiveness of regulatory compliance.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Modeling an IT Support for Handling Serious Adverse Events in Clinical Trials. Fonck, S., Deserno, T.[2018]

An interactive decision-making aid was developed for patients with atrial fibrillation (AF) using a user-centered design approach, incorporating feedback from both experts and AF patients.

Preliminary evaluations suggest that this decision aid, which includes visualizations, could effectively help patients make informed treatment decisions regarding their AF management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Designing for patient decision-making: Design challenges generated by patients with atrial fibrillation during evaluation of a decision aid prototype. Fanio, J., Zeng, E., Wang, B., et al.[2023]

A study involving 15 patients with atrial fibrillation and 5 clinicians revealed that while patients experienced low decisional conflict and regret regarding catheter ablation, they felt under-informed and lacked agency in the decision-making process.

Patients expressed a strong preference for an interactive, web-based decision aid that provides clear evidence and personalized risk assessments, indicating a need for tools that enhance decision quality and patient engagement in treatment choices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

'Replace uncertainty with information': shared decision-making and decision quality surrounding catheter ablation for atrial fibrillation. Reading Turchioe, M., Mangal, S., Ancker, JS., et al.[2023]

References

1. Spain pubmed.ncbi.nlm.nih.gov

Clinical value of a tool for managing oral anticoagulation in nonvalvular atrial fibrillation in primary health care. Randomized clinical trial. [2023]

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

The ENHANCE-AF clinical trial to evaluate an atrial fibrillation shared decision-making pathway: Rationale and study design. [2022]

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

Shared Decision Making Tools for People Facing Stroke Prevention Strategies in Atrial Fibrillation: A Systematic Review and Environmental Scan. [2021]

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

Stroke prevention in patients with atrial fibrillation: disease burden and unmet medical needs. [2012]

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

Evaluating the Effect of a Patient Decision Aid for Atrial Fibrillation Stroke Prevention Therapy. [2020]

6. New Zealand pubmed.ncbi.nlm.nih.gov

Patient-Reported Outcome Measures in Safety Event Reporting: PROSPER Consortium guidance. [2022]

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

National Estimates of Emergency Department Visits for Antibiotic Adverse Events Among Adults-United States, 2011-2015. [2020]

8. Switzerland pubmed.ncbi.nlm.nih.gov

Industry Assessment of the Contribution of Patient Support Programs, Market Research Programs, and Social Media to Patient Safety. [2020]

9. New Zealand pubmed.ncbi.nlm.nih.gov

Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets. [2022]

10. Netherlands pubmed.ncbi.nlm.nih.gov

Modeling an IT Support for Handling Serious Adverse Events in Clinical Trials. [2018]

11. Switzerland pubmed.ncbi.nlm.nih.gov

Designing for patient decision-making: Design challenges generated by patients with atrial fibrillation during evaluation of a decision aid prototype. [2023]

12. England pubmed.ncbi.nlm.nih.gov

'Replace uncertainty with information': shared decision-making and decision quality surrounding catheter ablation for atrial fibrillation. [2023]

13. England pubmed.ncbi.nlm.nih.gov

Clinical pre-test of a computerised antithrombotic risk assessment tool for stroke prevention in atrial fibrillation patients: giving consideration to NOACs. [2017]

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

Use of regression modeling to simulate patient-specific decision analysis for patients with nonvalvular atrial fibrillation. [2017]