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Review
. 2021 Jun 16;117(7):1-21.
doi: 10.1093/cvr/cvab147.

Optimizing indices of atrial fibrillation susceptibility and burden to evaluate atrial fibrillation severity, risk and outcomes

Giuseppe Boriani  1 , Marco Vitolo  1   2   3 , Igor Diemberger  4 , Marco Proietti  2   5   6 , Anna Chiara Valenti  1 , Vincenzo Livio Malavasi  1 , Gregory Y H Lip  2   7
Affiliations
Review

Optimizing indices of atrial fibrillation susceptibility and burden to evaluate atrial fibrillation severity, risk and outcomes

Giuseppe Boriani et al. Cardiovasc Res. .

Erratum in

Abstract

Atrial fibrillation (AF) has heterogeneous patterns of presentation concerning symptoms, duration of episodes, AF burden, and the tendency to progress towards the terminal step of permanent AF. AF is associated with a risk of stroke/thromboembolism traditionally considered dependent on patient-level risk factors rather than AF type, AF burden, or other characterizations. However, the time spent in AF appears related to an incremental risk of stroke, as suggested by the higher risk of stroke in patients with clinical AF vs. subclinical episodes and in patients with non-paroxysmal AF vs. paroxysmal AF. In patients with device-detected atrial tachyarrhythmias, AF burden is a dynamic process with potential transitions from a lower to a higher maximum daily arrhythmia burden, thus justifying monitoring its temporal evolution. In clinical terms, the appearance of the first episode of AF, the characterization of the arrhythmia in a specific AF type, the progression of AF, and the response to rhythm control therapies, as well as the clinical outcomes, are all conditioned by underlying heart disease, risk factors, and comorbidities. Improved understanding is needed on how to monitor and modulate the effect of factors that condition AF susceptibility and modulate AF-associated outcomes. The increasing use of wearables and apps in practice and clinical research may be useful to predict and quantify AF burden and assess AF susceptibility at the individual patient level. This may help us reveal why AF stops and starts again, or why AF episodes, or burden, cluster. Additionally, whether the distribution of burden is associated with variations in the propensity to thrombosis or other clinical adverse events. Combining the improved methods for data analysis, clinical and translational science could be the basis for the early identification of the subset of patients at risk of progressing to a longer duration/higher burden of AF and the associated adverse outcomes.

Keywords: AF burden; AF susceptibility; Atrial fibrillation; Stroke.

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Figures

None
Graphical abstract
Figure 1
Figure 1
The complex interplay between atrial fibrillation, underlying atrial and ventricular factors, comorbidities, and adverse outcomes (stroke, cardiovascular mortality). AF, atrial fibrillation; CAD, coronary artery disease; CKD, chronic kidney disease; LV, left ventricular; OSA, obstructive sleep apnoea; PAD, peripheral artery disease.
Figure 2
Figure 2
Time pattern and type of atrial fibrillation. AF, atrial fibrillation; AHRE, atrial high-rate episodes; APC, atrial premature contraction; min, minutes; SR, sinus rhythm.
Figure 3
Figure 3
Atrial fibrillation time clock: the effect of a series of comorbidities affecting AF temporal pattern. AF, atrial fibrillation; AMI, acute myocardial ischaemia; CAD, coronary artery disease.
See this image and copyright information in PMC

References

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