Data-driven SDM to Reduce Symptom Burden in AF

Columbia University

Status: Active, not recruiting

Conditions: Atrial Fibrillation; Patient Engagement

Treatments: Other: Shared decision-making tool

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT04993807

K99NR019124 (U.S. NIH Grant/Contract)

AAAU2028

19-11021059

Details and patient eligibility

About

This study is a single-group feasibility study evaluating decision aid visualizations which display common post-ablation symptom patterns as a tool for shared decision-making. The specific aim of the clinical trial is to evaluate the feasibility of putting the visualizations into clinical practice (n=75). The hypothesis is that patients will report low decisional conflict and decision regret and high satisfaction with their decision about whether to undergo an ablation or not.

Full description

Atrial fibrillation (AF) is the most common heart rhythm disorder, and nearly 90% of patients experience symptoms such as shortness of breath that directly impair their health-related quality of life (HRQoL). Catheter ablation is a minimally invasive, surgical procedure that is routinely performed to treat AF and associated symptoms with the goal of improving HRQOL, but also carries potentially serious risks. Shared decision-making (SDM), in which treatment decisions are aligned based on high quality evidence and patient values and goals of care, is a widely encouraged practice for navigating complex healthcare decisions such as these. However, SDM around rhythm and symptom management does not routinely occur due to a lack of detailed evidence about symptom improvement post-ablation, and a lack of decision aids to communicate evidence to patients. The overarching goal of this award is to create an interactive patient decision aid composed of established evidence from clinical trials together with novel "real world" evidence about symptom improvement post ablation mined from electronic health records (EHRs).

The investigators propose to use "real-world evidence" drawn from electronic health records (EHRs) to characterize post-ablation symptom patterns, and display them in decision-aid visualizations to support shared decision-making (SDM). In this project, the investigators will first use natural language processing (NLP) and machine learning (ML) to extract and analyze symptom data from narrative notes in EHRs. The investigators will also employ a rigorous, user-centered design protocol created during the Principal Investigator's post-doctoral work to develop decision-aid visualizations. In the clinical trial, the investigators will evaluate the feasibility of implementing these interactive decision-aid visualizations in clinical practice.

Enrollment: 75 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Diagnosis of paroxysmal AF according to International Classification of Diseases, Tenth Revision (ICD-10)
• Scheduled consultation at NewYork-Presbyterian Hospital (NYP) to discuss catheter ablation
• Symptomatic AF at baseline
• Age 18 years and older
• Able to read and speak English
• Willing/able to provide informed consent

Exclusion criteria

• Asymptomatic AF
• Severe cognitive impairment
• Major psychiatric illness
• Concomitant terminal illness that would preclude participation

Trial design

Primary purpose: Other

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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75 participants in 1 patient group

Shared decision-making tool

Experimental group

Description:

Participants in this arm will view a shared decision-making tool while they are undergoing consultation to have an atrial fibrillation ablation.

Treatment:

Other: Shared decision-making tool