Atrial 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.

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/).

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.

NCT06600620.

To evaluate the impact of various antihypertensive drugs on secondary stroke prevention in a real-life setting.

Nationwide historic cohort study.

French healthcare system data (SNDS).

Adults hospitalised for ischaemic stroke between 2014 and 2015 were followed up until December 2021 and stratified based on the presence of atrial fibrillation (AF).

Risk of stroke recurrence was assessed using a time-dependent Cox cause-specific model accounting for changes in drug exposure. We also investigated the risk of major adverse cardiovascular events (MACE) or all-cause death. Models were adjusted on stroke characteristics, coprescriptions and co-morbidities, at inclusion and across follow-up.

Among 54 764 patients without AF (median age 71; 46% women) and 17 960 with AF (median age 79; 51% women), stroke recurrence occurred in 11% and 13%, respectively. In non-AF patients, reduced recurrence risk was associated only with use of calcium channel blockers (adjusted HR (aHR) 0.91, 95% CI 0.86 to 0.97), thiazide diuretics (aHR 0.90, 95% CI 0. 83 to 0.97), loop diuretics (aHR 0.86, 95% CI 0.77 to 0.95) and potassium-sparing agents (aHR 0.83, 95% CI 0.70 to 0.98). In AF patients, only potassium-sparing agents (aHR 0.82, 95% CI 0.69 to 0.99) were associated with reduced recurrence risk. All antihypertensive drugs, apart from loop diuretics, were associated with a reduced risk of MACE or all-cause death.

In this large cohort, only diuretics and calcium channel blockers were associated with a reduced risk of recurrent stroke. Most antihypertensive drugs, however, may be more effective in overall cardiovascular prevention.