Not provided
Not provided
Not provided
Not provided
Not provided
logistical issues
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| University of Copenhagen | OTHER |
| Isansys Lifecare LTD | UNKNOWN |
| University of Liverpool | OTHER |
| Liverpool John Moores University |
Not provided
Not provided
Not provided
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.
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients admitted or referred to Critical Care (NOTE-AF ICU) | Patients admitted or referred to Critical Care |
| |
| Patients admitted to hospital with acute heart failure (NOTE-AF HF) | Patients admitted to hospital with acute heart failure |
| |
| Patients admitted to Emergency Services with sepsis or infection (NOTE-AF Sepsis) | Patients admitted to Emergency Services with sepsis or infection |
| |
| Patients post upper gastrointestinal surgery (NOTE-AF PULSE-GI) | Patients post upper gastrointestinal surgery |
| |
| Patients post vascular interventions (NOTE-AF Vasc) | Patients post vascular interventions |
| |
| Patients with acute respiratory failure (NOTE-AF Resp) |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Monitoring patients heart rythms with a wireless patch device | Device | The investigators will collect data in patients at high risk of atrial fibrillation (AF) without a known history of AF to determine clinical predicators of AF. This data will be used to generate virtual digital twins to to predict clinical and subclinical episodes of AF |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 1) Number of participants with device detected AF lasting greater than 30 seconds | 48 months |
| 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. | Number of episodes of AF and duration of each AF episode | 48 months |
| Measure | Description | Time Frame |
|---|---|---|
| Length of hospital stay | As measured in days and hours | 48 months |
| Hospital readmissions within 90 days | Number of readmissions and admission diagnosis of hospital readmissions |
Not provided
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:
Exclusion Criteria:
Not provided
Not provided
Not provided
Patients admitted to hospital with a variety of medical conditions
Not provided
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Liverpool university foundation trust | Liverpool | United Kingdom | ||||
| Liverpool University hospital Foundation trust |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41485778 | Derived | Essa H, Johnston B, Lip GYH, Ortega-Martorell S, Williams K, Welters ID; TARGET Consortium. Intelligent monitoring to predict atrial fibrillation (NOTE-AF): clinical study 1 for the 'Health virtual twins for the personalised management of stroke related to atrial fibrillation (TARGET)' project - a protocol for a prospective cohort analysis. BMJ Open. 2026 Jan 3;16(1):e099658. doi: 10.1136/bmjopen-2025-099658. |
Not provided
Not provided
Undecided at this point in time
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D001281 | Atrial Fibrillation |
| ID | Term |
|---|---|
| D001145 | Arrhythmias, Cardiac |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D010335 | Pathologic Processes |
Not provided
Not provided
| OTHER |
Not provided
Not provided
Not provided
Blood samples
Patients with acute respiratory failure |
|
| Patients admitted after acute stroke (NOTE-AF stroke) | Patients admitted after acute stroke |
|
|
| 48 months |
| Recurrence of AF episodes | Recurrence of AF post discharge as gathered from primary care and secondary care records. | 48 months |
| Hospital and 90-day Mortality | Inhospital and 90-day mortality | 48 months |
| Time spent in AF | Length of time in AF whilst on monitoring device | 48 months |
| Number of AF episodes | Number of AF episodes whilst on monitoring device | 48 months |
| Complications of AF | Inhospital and 90 day complications of AF such as stroke, thromboembolic disease & heart failure | 48 months |
| High sensitivity Troponin concentrations in patients with AF episodes | Troponin changes (measured by Hs-Trop T) in patients with episodes of AF | 48 months |
| Echocardiographic changes in patients with AF episodes | 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 | 48 months |
| mHealth App Usability Questionnaire | MAUQ score of patients with wireless observations | 48 months |
| Percentage change of troponin concentrations in patients with and without episodes of AF | Change in troponin levels in patients with and without episodes of AF | 48 months |
| Time wireless continuous vital signs monitoring device is attached | Measured in hours and minutes | 48 months |
| Number of cardiovascular alerts | Number of cardiovascular alerts registered by device | 48 months |
| Number of non-cardiovascular alerts | Number of non-cardiovascular alerts registered by device | 48 months |
| Number of alerts reflecting clinical changes | Number of alerts via continuous vital signs monitoring device | 48 months |
| Number of alerts reflecting artefacts or non-clinical events | Number of alerts reflecting clinical deterriorations versus number of alerts reflecting clinical deterrioation | 48 months |
| RWMA score, atrial size and volume, left ventricular strain rate, standard echocardiographic measurements as per british society of echocardiography recommendations | Echocardiographic predictors of AF | 48 months |
| Change in inflammatory markers white cell count, C-reactive protein and procalcitonin over time | Change in blood tests in patients with and without new-onset AF | 48 months |
| Liverpool |
| United Kingdom |
| D013568 |
| Pathological Conditions, Signs and Symptoms |