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Remote Cardiovascular Monitoring in Post-TAVI Patients (TELE-TAVI)

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Imperial College London

Status

Enrolling

Conditions

Transcatheter Aortic Valve Implantation
Aortic Valve Stenosis
Cardiac Conduction Disturbance
Digital Health
TAVI

Treatments

Other: Standard Medical Therapy
Other: Remote Patient Monitoring Strategy

Study type

Interventional

Funder types

Other

Identifiers

NCT06602609
24HH8773

Details and patient eligibility

About

The goal of this randomised clinical trial is to utilise a remote monitoring algorithm to gather essential clinical data, aiming to guide the management of post-Transcatheter Aortic Valve Implantation (TAVI) patients and reduce both postprocedural hospital length of stay and readmissions. This strategic integration of technology aims to address gaps identified in previous studies and enhance the effectiveness of post-TAVI patient care. One significant concern after TAVI is the development of heart conduction abnormalities on the ECG and abnormal rhythms, such as heart block, potentially requiring permanent pacemaker insertion. Addressing these rhythm issues is crucial for reducing the overall length of stay.

The main question it aims to answer is: Does a remote patient monitoring protocol-driven strategy reduce post-TAVI hospital length of stay and adverse events? Participants post-TAVI procedure and eligible for same-day discharge, as determined by their primary cardiologist, will be randomized upon informed consent into the active arm (remote monitoring) or control group (standard of care).

Participants in the active arm will: receive four remote monitoring devices. Receive support from a validated clinical decision-making algorithm for further management.

Participants in the control group will: adhere to the best standard of care as per current practice.

Researchers will compare the active arm to the control group to see if the remote patient monitoring protocol-driven strategy reduces post-TAVI hospital length of stay and adverse events.

Full description

Given the susceptibility of post-TAVI patients to bradyarrhythmias and extended hospitalisation is not economically plausible, a shift toward remote monitoring could be both justifiable and has the potential to enhance outcomes and able to predict deterioration following TAVI. Despite the well-characterized high safety and effectiveness profile of TAVI across various surgical risk groups, the adoption of same-day discharge remains limited. Addressing post-TAVI arrhythmias is crucial to minimising the total postprocedural length of stay. Therefore, by embracing a minimalist approach and leveraging the vastly grown operators experience, the current post-TAVI care could be further enhanced with the integration of remote monitoring. This strategic shift aligns with the objective of optimising care by the ability to predict deterioration that can be treated to avoid in-hospital care.

Technology-enabled patient care and monitoring has been steadily improving, and AI-enabled remote patient monitoring devices has brought a crucial advantage to an outside-of-hospital cardiac investigation. The potential to remotely detect any adverse effects that patients might develop after the procedure is promising. There is an increased uptake of ECG telemetry, which might serve as a valuable tool that may significantly reduce hospital LOS at 30 days and possibly could reduce 3 months' adverse outcomes. This approach may enhance patient care and optimise post-TAVI outcomes through an efficient patient-tailored approach. Several studies have utilised remote or ambulatory ECG for post-TAVI on different clinical outcomes - however, to date, no RCT has been conducted using this approach. The findings of remote ECG monitoring studies are inconsistent with various monitoring periods and patient selection criteria. Although such studies utilised ECG monitoring, they did not integrate a strategy or pathway for out-of-hospital monitoring and clinical decision algorithms during home monitoring. To date, the TELE-TAVI trial will be the first monitoring study to incorporate live event detection and alert notification system supported by the bespoke Smartcardia 7-lead continuous ECG patch monitor in an RCT.

In order to better understand the impact of remote patient monitoring after TAVI, this trial proposes the utilisation of a remote monitoring algorithm to provide essential clinical data for guiding management, with the primary aim of reducing postprocedural hospital length of stay and readmissions. This strategic technology integration aims to address the gaps identified in previous studies and enhance the effectiveness of post-TAVI patient care.

Thus, it has been hypothesised that:

  1. A remote patient monitoring protocol-driven strategy for same-day discharge, post-TAVI, is superior to the current standard of care follow-up in reducing cumulative 30-day hospital length of stay (LOS).
  2. The novel remote patient monitoring strategy, supported by a clinical decisions algorithm, is more effective at prioritising patient presentations and optimising outcomes through early identification of adverse events, such as the need for a pacemaker, when compared to the standard of care.

The principal aims of this clinical randomised trial are:

  • To facilitate a safe same-day discharge after TAVI through a remote monitoring approach in order to reduce postprocedural hospitalisation.
  • To integrate an algorithm-based clinical decision-making protocol that enhances remote monitoring and the detection of post-TAVI adverse events, particularly focusing on cardiac conduction complications.
  • To test the effectiveness of the remote monitoring strategy in the cost-effectiveness of post-TAVI patients' hospital admission and re-admission.

Enrollment

216 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • Patient age must be 18 years or above.
  • Post-TAVI patients without serious complications hindering same-day discharge, including vascular, neurological, and cardiac postprocedural complications necessitating inpatient care:
  • Vascular: Controlled with adequate vascular hemostasis.
  • Neurological: Conscious and oriented, not under the effect of residual sedation.
  • Cardiac: No significant pericardial effusion.
  • Elective non-emergent patient admission and nonelective cases fitting discharge criteria.
  • Comfortable ambulation post-procedure.
  • The patient or their caregiver must have access to a smart device.
  • Ability to provide informed consent (the patient must be alert and oriented for consent).
  • Adequate social support.

Exclusion criteria

  • Hemodynamically unstable post-TAVI condition, periprocedural cardiac arrest, cerebral vascular accident, acute kidney injury (AKI) and major/life-threatening bleeding requiring blood transfusion.
  • Patients with a permanent (pre-existing or new) implanted pacing device.
  • Procedural failure in device delivery.
  • Major vascular access complications require patients to stay.
  • Inability to engage with the technology.
  • Significant communication barrier.

Trial design

Primary purpose

Treatment

Allocation

Randomized

Interventional model

Parallel Assignment

Masking

None (Open label)

216 participants in 2 patient groups

Control Group
Other group
Description:
The control group adheres to the best standard of care as per current practice.
Treatment:
Other: Standard Medical Therapy
Remote Monitoring Group
Active Comparator group
Description:
Those in the remote monitoring group will be given four devices, which will help the research team make decisions about their health condition based on the data collected. The active arm group will wear the continuous ECG device for 72 hours from discharge and will be instructed to acquire a daily 12-lead ECG and record vital signs until day 7 of hospital discharge. Additionally, participants will utilise 12-lead ECG and vital signs will be measured only when experiencing symptoms.
Treatment:
Other: Remote Patient Monitoring Strategy

Trial contacts and locations

1

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Central trial contact

Badr Alharbi, BScEMS, MSc

Data sourced from clinicaltrials.gov

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