Cloud-Based Mapping for Personalized Ablation

NCT ID: NCT04127123

Last Updated: 2021-05-07

Basic Information

• Recruitment Status: WITHDRAWN
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-07-03
• Study Completion Date: 2021-01-11

Brief Summary

Atrial fibrillation is a serious public health issue that affects over 5 million Americans in whom it may cause skipped beats, dizziness, stroke and even death. This study seeks to improve our understanding of the causes of atrial fibrillation and to design new and more effective therapy for this heart rhythm disorder.

Detailed Description

This project will focus on the development of a novel paradigm for electrophysiologic data analysis and interpretation using cloud-based computing resources and mobile technology. Currently, electrophysiologic data gathered during a procedure is analyzed by the operator using multiple separate desk-based computer systems in the electrophysiology laboratory. The investigators propose that advances in cloud-based computing resources and network connectivity should apply a mobile paradigm to apply to invasive electrophysiologic procedures.

This project will provide proof-of-concept that open-access software the investigators have developed and made available online could be used, via a mobile phone interface, to identify sites in the heart where therapy is effective. At no time will patient therapy be guided by this system. The investigators will pursue therapy using only clinical means. In parallel, a double-blinded team will analyze data in real time using our online software visualized on a smartphone. Only when the case is concluded will the data be unblinded, to determine if the mobile system was accurate in real time.

Thus, development and testing of the cloud-based computing system is designed only to establish feasibility of the paradigm, followed by improvement of computational modeling algorithms. The data that is collected will add to the investigators' existing unique catalogue of multimodal (structural, clinical, and electrophysiologic) data.

The importance of this novel paradigm is to move from analyzing large volumes of data in isolation to creating a mobile platform, and to allow scalability to increase access, such as to underdeveloped medical centers.

Conditions

Atrial Fibrillation; Arrhythmias, Cardiac

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• men and women of any ethnicity
• aged 21-80 years
• undergoing ablation of atrial fibrillation at Stanford University
• failed or be intolerant of ≥ 1 anti-arrhythmic drug or not willing to accept antiarrhythmic drug therapy.

Exclusion Criteria

• active coronary ischemia or decompensated heart failure
• atrial or ventricular clot on trans-esophageal echocardiography
• pregnancy (to minimize fluoroscopic exposure)
• inability or unwillingness to provide informed consent
• rheumatic valve disease (because it results in a unique AF phenotype)
• thrombotic disease or venous filters
• significantly reduced kidney function

• Minimum Eligible Age: 21 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Stanford University

OTHER

Sponsor Role: lead

Responsible Party

Sanjiv Narayan, MD, PhD

Professor of Medicine

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Sanjiv Narayan, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Stanford University

Locations

Stanford University

Stanford, California, United States

Countries

United States

Other Identifiers

49800

Identifier Type: OTHER

Identifier Source: secondary_id

49800

Identifier Type: -

Identifier Source: org_study_id