Remote Patient Management of Cardiac Implantable Electronic Devices - Brady Devices

Ratika Parkash11 sites in 1 country848 target enrollmentSeptember 15, 2020

ConditionsPacemaker

Overview

Phase: N/A
Intervention: Not specified
Conditions: Pacemaker
Sponsor: Ratika Parkash
Enrollment: 848
Locations: 11
Primary Endpoint: Time to major adverse cardiac event (primary safety outcome)
Status: Recruiting
Last Updated: last year

Overview Investigators Eligibility Criteria Outcomes Sites Similar Trials

Overview

Brief Summary

While remote monitoring (RM) technology is currently available and has permitted surveillance and device assessment from any patient location, the use has been inconsistent in Canada, where only 8500 out of a potential 120 000 patients with cardiac implantable electronic devices (CIEDs) are enrolled in this program. This technology is in widespread use worldwide for all CIEDs but in Canada, it is utilized primarily for implantable defibrillators, but not pacemakers. Whereas most of the trials were designed to evaluate the efficacy of RM in implantable cardioverter defibrillator (ICD) patients, in the pacemaker (PM) population, there has been work performed already to demonstrate an increase in detection of frequency of adverse clinical events and a reduction in reaction time to those events by RM. Based on all the available literature, it appears that RM benefits both patients and healthcare systems. Overall, studies have demonstrated that RM can be used safely in all device patient-populations, with the exception of pacemaker-dependent patients.

There have been no studies that have evaluated RM only follow up, nor have there been any studies evaluating pacemaker-dependent patients. This study that will safely assess the use of RM only, with in-clinic visits when necessary, that uses the patient-centered electronic platform developed by the Cardiac Arrhythmia Network of Canada (CANet) to perform PM follow up safely, in a more cost-effective manner.

Detailed Description

This is a Canadian multicenter randomized controlled trial to assess remote patient management. Patients will be randomized in a 1:1 fashion, stratified by pacemaker dependence and by center to either remote patient management of standard of care. Remote patient management (intervention group): * Patients will be followed by remote monitoring only. Transmissions will occur at six monthly intervals, with no in-clinic visits. If there is an actionable event on the remote transmission, patients will be seen at their closest community device clinic. The proposed intervention would eliminate any routine visits. * Newly implanted pacemaker patients will be seen after the initial implant at the hub site, within 48 hours and then within 3 months to optimize programming for subsequent RM visits. Thereafter, all follow-up will occur through remote monitoring every six months. Unscheduled remote transmissions may also occur. Using a combination of remote monitoring, and novel technology, VIRTUES, patients will be managed outside of the clinic. * Patients will be provided with real-time knowledge of success of their remote transmission and the status of the remote through a web-based portal called VIRTUES. * Virtual patient record - all patient information in the intervention arm will be contained within an information cloud that will be accessible by patients; access to their own data may be granted by the patient themselves to permit delivery of care remotely, and to permit after hours follow up to be performed by the on call personnel. Standard of care (comparison group): The standard arm will be required to have in-clinic visits at yearly intervals. These patients would not have access to the virtual patient cloud or to remote monitoring. Pacemaker programming: All patients in both arms will undergo programming as per the ASSERT II protocol (previously published) to optimize pacemaker parameters and detection of atrial and ventricular high rate episodes.

Registry

clinicaltrials.gov

Start Date

September 15, 2020

End Date

June 30, 2025

Last Updated

last year

Study Type

Interventional

Study Design

Parallel

Sex

All

Investigators

Sponsor

Ratika Parkash

Responsible Party

Sponsor Investigator

Principal Investigator

Ratika Parkash

Staff Cardiologist Electrophysiologist

Nova Scotia Health Authority

Eligibility Criteria

Inclusion Criteria

•Patients with a Medtronic or Abbott pacemaker capable of remote monitoring.
•Able to provide consent.
•Age \>/= 18 years

Exclusion Criteria

•No access to a family physician or general practioner
•Participation in the RPM CIED pilot study
•Unreliable automated capture verification function by the device in pacemaker-dependent patients

Outcomes

Primary Outcomes

Time to major adverse cardiac event (primary safety outcome)

Time Frame: 18 months

Time to major adverse events (death, stroke, hospitalization for a cardiovascular cause, syncope or device-related emergency department visits). Survival free from the primary outcome will be estimated by the Kaplan-Meier method and compared, using the log-rank test. Cox proportional hazard regression analysis will be used to estimate the likelihood of survival, after verifying the proportional hazard assumption.

Cost effectiveness

Time Frame: 18 months

Analysis will take the form of a cost utility analysis with cost effectiveness assessed in terms of the incremental cost per quality life year (QALY). Analysis will incorporate data on resource use and patient's utility values for the period from initiation of remote patient management to 12 months follow-up. Resource use associated with the remote patient management strategy will be estimated through detailed microcosting and will be contrasted with the costs of standard monitoring. QALYs will be estimated using the standard area under the cover methodology, controlling for baseline utility. Total costs and QALYs for each patient will be estimated with multiple imputation for missing data. The incremental costs and QALYs and incremental cost effectiveness ratios associated with remote patient management and the uncertainty around these will be derived through non-parametric bootstrapping.