Intelligent Monitoring to Predict Atrial Fibrillation

Suspended

• Conditions: Atrial Fibrillation New Onset; Atrial Fibrillation; Postoperative Cardiovascular Complications

• Registration Number: NCT06600620
• Lead Sponsor: Liverpool University Hospitals NHS Foundation Trust

Brief Summary

Atrial Fibrillation (AF) is the commonest arrhythmia worldwide, affects 5% of people over the age of 65 and increases the risk of stroke and heart failure. The investigators aim to detect clinical and subclinical episodes of atrial fibrillation lasting \>30 seconds to develop risk prediction models to identify patients at high risk for ischaemic stroke.

Detailed Description

Atrial Fibrillation (AF) is the commonest arrhythmia worldwide, affects 5% of people over the age of 65 and increases the risk of stroke and heart failure. Among acutely unwell patients; arrhythmias and myocardial injury are common and associated with increased mortality, morbidity, and healthcare costs. Cardiovascular comorbidities in these high-risk patients include hypertension (47%), dyslipidaemia (29%) and ischaemic heart disease (11%).

The investigators aim to detect clinical and subclinical episodes of atrial fibrillation lasting 30 seconds to develop risk prediction models to identify patients at high risk for ischaemic stroke. Data will serve to develop and validate bedside clinical decision support tools and digital twins. Patients who develop episodes of AF as part of acute illness, will suffer further episodes of AF within one year in over 20% of cases with 27% progressing to paroxysmal/permanent AF. The true incidence of AF is unknown in acutely unwell patients as a significant percentage of AF episodes remain undetected with conventional intermittent monitoring. Patients experiencing short self-terminating episodes of AF carry a 5-fold risk of developing continuous AF and double the risk of stroke and thromboembolic events. Patients suffering episodes of AF often remain asymptomatic but are at increased risk of heart failure and death at one year. Compared to routine intermittent manual measurement of vital signs, wireless continuous vital sign monitoring systems (wCVSM) detect deviations instantaneously with the option of alerting clinical staff in real time via mobile phone applications. Accurate categorization of alerts into false and true events is essential for developing intelligent software that can be embedded into monitoring systems. Continuous ECG and vital signs monitoring can detect AF episodes more reliable, trigger timely investigations and support longer term treatment plans.

Changes in patient pathways and introduction of novel devices to alert healthcare staff on the potential of clinical events require buy-in from all stakeholders. It is therefore essential to evaluate user acceptance and to determine perceptions of users before rolling out a novel patient pathways or implementation of a new device within an organization. The investigators therefore wish to explore users\' views of the device, wearing the device and potential areas for improvement using questionnaires for patients and health care staff and by conducting semi-structured interviews with healthcare staff.

Primary objective To determine the true cardiovascular event rate (defined as at least one of the following criteria: episodes of AF, New Regional Wall Motion Abnormalities, raised cardiac biomarkers hs-troponin T and NT-pro-BNP) versus false cardiovascular events detected by continuous wireless remote monitoring.

Secondary objective To determine patient acceptability and usability for health care professionals of a novel remote monitoring device with automated alert function.

Study Timeline

• Study First Submitted: 2024-09-12
• Study First Submitted QC: 2024-09-16
• Study First Posted: 2024-09-19
• Study Start: 2024-09-23
• Status Verified: 2025-06
• Last Update Submitted: 2025-09-08
• Last Update Posted: 2025-09-15
• Primary Completion: 2028-07-31
• Study Completion: 2028-08-01

Recruitment & Eligibility

• Status: SUSPENDED
• Sex: All
• Target Recruitment: 1200

Inclusion Criteria

• Adult patients ≥50 years

• Estimated risk of developing new episodes of AF greater than 5%

• Sinus rhythm at presentation

• One of the following acute conditions:

  • Patients admitted or referred to Critical Care (NOTE-AF ICU)
  • Patients admitted to hospital with acute heart failure (NOTE-AF HF)
  • Patients admitted to Emergency Services with sepsis or infection (NOTE-AF Sepsis)
  • Patients post upper gastrointestinal surgery (NOTE-AF PULSE-GI)
  • Patients post vascular interventions (NOTE-AF Vasc)
  • Patients with acute respiratory failure (NOTE-AF Resp)
  • Patients admitted after acute stroke (NOTE-AF stroke)

Exclusion Criteria

• Atrial fibrillation or atrial flutter at the time of screening
• Patients in atrial fibrillation or atrial flutter at time of preoperative assessment or admission to hospital
• Paced cardiac rhythm
• Inability to obtain consent
• Allergy to plaster or silicone
• Expected hospital stay less than 48 hours

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
To determine the incidence of clinical and subclinical episodes of AF in acutely unwell patients and to generate data for the development and validation of virtual twins and clinical decision support tools.	48 months	Number of episodes of AF and duration of each AF episode

Secondary Outcome Measures

Name	Time	Method
Length of hospital stay	48 months	As measured in days and hours
Hospital readmissions within 90 days	48 months	Number of readmissions and admission diagnosis of hospital readmissions
Recurrence of AF episodes	48 months	Recurrence of AF post discharge as gathered from primary care and secondary care records.
Hospital and 90-day Mortality	48 months	Inhospital and 90-day mortality
Time spent in AF	48 months	Length of time in AF whilst on monitoring device
Number of AF episodes	48 months	Number of AF episodes whilst on monitoring device
Complications of AF	48 months	Inhospital and 90 day complications of AF such as stroke, thromboembolic disease \& heart failure
High sensitivity Troponin concentrations in patients with AF episodes	48 months	Troponin changes (measured by Hs-Trop T) in patients with episodes of AF
Echocardiographic changes in patients with AF episodes	48 months	As measured by advanced echocardiographic parameters including but not limited to left atrial conduit strain, left atrial booster strain, left atrial stiffness and left atrial strain
mHealth App Usability Questionnaire	48 months	MAUQ score of patients with wireless observations
Percentage change of troponin concentrations in patients with and without episodes of AF	48 months	Change in troponin levels in patients with and without episodes of AF
Time wireless continuous vital signs monitoring device is attached	48 months	Measured in hours and minutes
Number of cardiovascular alerts	48 months	Number of cardiovascular alerts registered by device
Number of non-cardiovascular alerts	48 months	Number of non-cardiovascular alerts registered by device
Number of alerts reflecting clinical changes	48 months	Number of alerts via continuous vital signs monitoring device
Number of alerts reflecting artefacts or non-clinical events	48 months	Number of alerts reflecting clinical deterriorations versus number of alerts reflecting clinical deterrioation
Change in inflammatory markers white cell count, C-reactive protein and procalcitonin over time	48 months	Change in blood tests in patients with and without new-onset AF
RWMA score, atrial size and volume, left ventricular strain rate, standard echocardiographic measurements as per british society of echocardiography recommendations	48 months	Echocardiographic predictors of AF

Trial Locations

Locations (2)

Liverpool university foundation trust; 🇬🇧 Liverpool, United Kingdom

Liverpool University hospital Foundation trust; 🇬🇧 Liverpool, United Kingdom