The detection of atrial fibrillation (AF) paroxysms and its permanent form as well as the
prevention of AF-related strokes are major challenges in cardiology today. AF is often
silent or asymptomatic, but the risk of ischemic stroke seems to be similar regardless of
the presence or absence of symptoms. Asymptomatic AF patients are more likely to evade
diagnostic effort and without appropriate anticoagulation they are left vulnerable to
thromboembolism and ischemic stroke. Approximately one third of all ischemic strokes are
of an unknown cause. Recent studies have shown that more diligent monitoring of heart
rhythm with ambulatory devices after a cryptogenic stroke uncovers a high number of
silent AF episodes. Timely detection of silent AF is challenging and, therefore, a stroke
is still too often the first clinical manifestation of AF accounting for 22% of
AF-related strokes. In order to detect symptoms appearing at periodic or random
intervals, a capability for longer-term monitoring, e.g. for several days or weeks at a
time, is required. Implantable electrocardiogram (ECG) loop recorders perform well in AF
detection, but they are hardly feasible in large patient cohorts due to invasive nature
and costs. Thus, there is an unmet clinical need for better AF detection tools.
During the Automated Detection of Atrial Fibrillation via a Miniature Accelerometer and
Gyroscope (NoStroke) project (as well as other projects carried out simultaneously at
Department of Future Technologies, University of Turku, mainly funded by Tekes) a
smartphone application has been developed for the detection of AF, which is implemented
as a stand-alone solution to a common smartphone. The application utilizes the inbuilt
motion sensors of the smartphones without any need to external sensors or equipment, such
as electrodes or wires. This application (CardioSignal app) is a CE-marked medical device
and will be used in this project and it will provide a venue for a clinical showcase of
the application and more importantly, validate whether the application would be
beneficial in the clinical settings in this follow-up study.
Bed sensor (Emfit ltd) is a CE-marked device. It is based on ballistography method where
a heartbeat-induced recoil can be measured while the subject is lying on the bed.
Electromechanical membrane sensor recognizes and registers the recoil, and subsequently,
this data can be used to assess heart rate and other relevant vital function. Philips
Data Logger (PDL) measures continuously photoplethysmogram (PPG) and accelerometer data
from the top side of the wrist using green wavelength Light Emitting Diode (LED) and
3-axis accelerometer. Faros Holter-monitor measures single-lead ECG signal from the chest
and is attached via FastFix electrode.
The part I trial is open, prospective interventional trial. These recordings are used
merely to technical testing, validity evaluation and algorithm development purposes and
the data is not used in any clinical evaluation whatsoever.
The part II is a prospective, randomized, open-label, interventional study. Upon receipt
of the signed written informed consent and satisfactory documentation that the patient
met all inclusion and had no exclusion criteria, the study subjects will be randomized to
either intervention or control arm. The randomization allocation will take into account
the procedure subgroup of the patient (open-heart surgery, aortic valve replacement and
transcatheter aortic valve implantation (TAVI) or coronary bypass and percutaneous
coronary intervention (PCI)). In both subgroups the randomization will be done 1:1.
Patients will be randomized before hospital discharge.
The number of study subjects needed was estimated to be 300. Complying with the original
plan, interim analysis was performed when 100 cases were enrolled. Based on the interim
analysis results, the target enrollment number of patients was reduced to 150 patients
due to the high number of false positive alerts leading to high-workload protocol
inadequate for clinical use as such.
