Kalendarium

10 – 12 april

Kardiologisen seuran kevätkokous, Helsinki, Finland

17 – 19 april

25:e Svenska Kardiovaskulära Vårmötet, Göteborg

24 – 26 april

SFAM, Svensk Allmänmedicinsk Kongress, Uppsala

6 maj

Kardiologisten hoitajien koulutuspäivät (Utbildningsdagar för kardiologsjuksköterskor), Jyväskylä, Finland

16 – 17 maj

Kliinisen fysiologian hoitajien koulutuspäivät, Helsinki, Finland

Artificiell intelligens

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): pilot study of an electronic health record machine learning algorithm guided intervention to identify undiagnosed atrial fibrillation
Nadarajah R, Wahab A, Reynolds C, Raveendra K, Askham D, Dawson, R, Keene J, Shanghavi S, Lip G Y H, Hogg D, Cowan C, Wu J & Gale C P. 2023.

Introduction
Atrial fibrillation (AF) is associated with a fivefold increased risk of stroke. Oral anticoagulation reduces the risk of stroke, but AF is elusive. A machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)) developed to predict incident AF within 6 months using data in primary care electronic health records (EHRs) could be used to guide AF screening. The objectives of the FIND-AF pilot study are to determine yields of AF during ECG monitoring across AF risk estimates and establish rates of recruitment and protocol adherence in a remote AF screening pathway.

Methods and analysis
The FIND-AF Pilot is an interventional, non-randomised, single-arm, open-label study that will recruit 1955 participants aged 30 years or older, without a history of AF and eligible for oral anticoagulation, identified
as higher risk and lower risk by the FIND-AF risk score from their primary care EHRs, to a period of remote ECG monitoring with a Zenicor-ECG device. The primary outcome is AF diagnosis during ECG monitoring,
and secondary outcomes include recruitment rates, withdrawal rates, adherence to ECG monitoring and prescription of oral anticoagulation to participants diagnosed with AF during ECG monitoring. Ethics and dissemination
The study has ethical approval (the North West-Greater Manchester South Research Ethics Committee reference 23/NW/0180). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder’s open access policy.


An artificial intelligence basedmodel for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening
Hygrell T, Viberg F, Dahlberg E, Charlton P H, Kemp Gudmundsdottir K, Mant J, Lindman Hörnlund J, Svennberg E. 2023.

Abstract Aims
Screening for atrial fibrillation (AF) is recommended in the European Society of Cardiology guidelines. Yields of detection can be low due to the paroxysmal nature of the disease. Prolonged heart rhythm monitoring
might be needed to increase yield but can be cumbersome and expensive. The aim of this study was to observe the accuracy of an artificial intelligence (AI)-based network to predict paroxysmal AF from a normal sinus rhythm single-lead ECG.

Methods and results
A convolutional neural network model was trained and evaluated using data from three AF screening studies. A total of 478 963 single-lead ECGs from 14 831 patients aged ≥65 years were included in the analysis.
The training set included ECGs from 80% of participants in SAFER and STROKESTOP II. The remaining ECGs from 20% of participants in SAFER and STROKESTOP II together with all participants in STROKESTOP
I were included in the test set. The accuracy was estimated using the area under the receiver operating characteristic curve (AUC). From a single timepoint ECG, the artificial intelligence-based algorithm predicted paroxysmal AF in the SAFER study with an AUC of 0.80 [confidence interval (CI) 0.78-0.83], which had a wide age range of 65-90+years. Performance was lower in the age-homogenous groups in STROKESTOP I and STROKESTOP II (age range: 75-76 years), with AUCs of 0.62 (CI 0.61-0.64) and 0.62 (CI 0.58-0.65), respectively.

Conclusion
An artificial intelligence-enabled network has the ability to predict AF from a sinus rhythm single-lead ECG. Performance improves with a wider age distribution.