Abstract
Background and Aim: Rapid and sensitive detection of atrial fibrillation (AF) is of paramount importance
for initiation of adequate preventive therapy after stroke. Stroke Unit care includes
continuous electrocardiogram monitoring (CEM) but the optimal exploitation of the
recorded ECG traces is controversial. In this retrospective single-center study, we
investigated whether an automated analysis of continuous electrocardiogram monitoring
(ACEM), based on a software algorithm, accelerates the detection of AF in patients
admitted to our Stroke Unit compared to the routine CEM. Methods: Patients with acute ischemic stroke or transient ischemic attack were consecutively
enrolled. After a 12-channel ECG on admission, all patients received CEM. Additionally,
in the second phase of the study the CEM traces of the patients underwent ACEM analysis
using a software algorithm for AF detection. Patients with history of AF or with AF
on the admission ECG were excluded. Results: The CEM (n = 208) and ACEM cohorts (n= 114) did not differ significantly regarding
risk factors, duration of monitoring and length of admission. We found a higher rate
of newly-detected AF in the ACEM cohort compared to the CEM cohort (15.8% versus 10.1%,
P < .001). Median time to first detection of AF was shorter in the ACEM compared to
the CEM cohort [10 hours (IQR 0–23) versus 46.50 hours (IQR 0–108.25), P < .001]. Conclusions: ACEM accelerates the detection of AF in patients with stroke compared with the routine
CEM. Further evidences are required to confirm the increased rate of AF detected using
ACEM.
Key Words
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Article info
Publication history
Published online: February 11, 2020
Accepted:
January 11,
2020
Received in revised form:
December 30,
2019
Received:
July 22,
2019
Footnotes
Grant Support: Graham Dixon grant.
Identification
DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.104669
Copyright
© 2020 Elsevier Inc. All rights reserved.
