Evidence map›Paper›PMID 41485778›Full record

ArticleBMJ open2026

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.

Hani Essa, Brian Johnston, Gregory Y H Lip, Sandra Ortega-Martorell, Karen Williams, Ingeborg D Welters, TARGET Consortium

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06600620 (Intelligent Monitoring to Predict Atrial Fibrillation [NOTE-AF]), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06600620 suspendednot on this map

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

TypeobservationalSponsorLiverpool University Hospitals NHS Foundation TrustRan2024 to 2028Enrolled1,200ConditionsAtrial Fibrillation New Onset, Postoperative Cardiovascular Complications, Atrial FibrillationArmsMonitoring patients heart rythms with a wireless patch device
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hani EssaDepartment of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, UK hani.essa@liverpool.ac.uk.ORCID http://orcid.org/0000-0001-9445-2582
Brian JohnstonUniversity of Liverpool Faculty of Health and Life Sciences, Liverpool, UK.ORCID http://orcid.org/0000-0003-1634-3297
Gregory Y H LipLiverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, Merseyside, UK.ORCID http://orcid.org/0000-0002-7566-1626
Sandra Ortega-MartorellDepartment of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, UK.ORCID http://orcid.org/0000-0001-9927-3209
Karen WilliamsCritical Care Research, Royal Liverpool University Hospital, Liverpool, UK.ORCID http://orcid.org/0009-0007-2121-7742
Ingeborg D WeltersDepartment of Cardiovascular and Metabolic Medicine, University of Liverpool, Liverpool, UK.
TARGET Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAtrial Fibrillation (AF) is the most common arrhythmia worldwide affecting an estimated 5% of people over the age of 65 and is a leading cause of stroke and heart failure. Identification of patients at risk allows preventative measures and treatment before these complications occur. Conventional risk prediction models are static, do not have flexibility to incorporate dynamic risk factors and possess only modest predictive value. Artificial intelligence and machine learning-powered health virtual twin technology offer transformative methods for risk prediction and guiding clinical decisions. METHODS AND ANALYSIS: In this prospective observational study, 1200 patients will be recruited in two tertiary centres. Patients hospitalised with acute illnesses (sepsis, heart failure, respiratory failure, stroke or critical illness) and patients having undergone high-risk surgery (major vascular surgery, upper gastrointestinal surgery and emergency surgery) will be monitored with a patch-based remote wireless monitoring system for up to 14 days. Clinical and electrocardiographic data will be used for modelling the risk of new-onset AF. The primary outcome is episodes of AF >30 s and will be described as ratio of episodes/patient and as percentage of patients having episodes of AF. Secondary outcomes include 30-day and 90-day readmission rates and complications of AF.The aim of this study is to generate data for the development and validation of health virtual twins predicting onset of AF in an at-risk population. The intelligent monitoring to predict atrial fibrillation (NOTE-AF) study is part of the TARGET project, a Horizon Europe funded programme which includes risk prediction, diagnosis and management of AF-related stroke (https://target-horizon.eu/). ETHICS AND DISSEMINATION: The study has received approval by the Health Research Authority and the National Research Ethics Service (REC reference 24/NW/0170, IRAS project ID: 342528) in the UK and has been registered on clinicaltrials.gov (NCT06600620). Results will be disseminated as outlined in the TARGET protocol to communicate project ideas, activities and results to diverse audiences. TRIAL REGISTRATION NUMBER: NCT06600620.

Indexed as

Artificial IntelligenceAtrial FibrillationStrokeAgedFemaleHumansMachine LearningMaleMiddle AgedObservational Studies as TopicProspective StudiesRisk AssessmentRisk FactorsCARDIOLOGYMachine LearningPatientsWearable Electronic Devices

Identifiers

PMID41485778
PMCPMC12766758

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.