The electrocardiogram as a predictor of successful pharmacological cardioversion and progression of atrial fibrillation

Zeemering, Stef; Lankveld, Theo; Bonizzi, Pietro; Limantoro, Ione; Bekkers, Sebastiaan C.A.M.; Crijns, Harry J.G.M.; Schotten, Ulrich

doi:10.1093/europace/eux234

Cited by 18 publications

(30 citation statements)

References 19 publications

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“…CA outcome prediction was improved by combining clinical data with NDI, thus linking body surface AF organization as estimated by our marker with ablation therapy effectiveness. The results obtained were comparable with those reported in previous studies (Lankveld et al, 2016b ; Zeemering et al, 2017 ) and underlined the relevance of body surface complexity as a marker of ablation therapy impact. Also f-wave amplitude contributed to increase multidimensional classification accuracy, as proved elsewhere (Lankveld et al, 2016b ; Zeemering et al, 2017 ).…”

Section: Discussionsupporting

confidence: 90%

“…This finding may appear in contrast with our clinical intuition, which correlates higher atrial amplitude with a more homogeneous and organized wavefront of tissue depolarization. However, similar evidence was also found in Zeemering et al ( 2017 ), pointing out that higher f-wave amplitude may predict AF recurrence after pharmacological cardioversion. Due to the difficult interpretability of the physiological background, such aspects deserve more detailed investigation.…”

Section: Discussionsupporting

confidence: 83%

“…The ability of clinical parameters alone to predict CA effectiveness and predict larger CA interventions (in terms of procedural time and targets) was overall limited, in line with previous research (Lankveld et al, 2016b ; Zeemering et al, 2017 ). As pointed out in those studies, information about patient's clinical background may be incomplete or imprecise.…”

Section: Discussionsupporting

confidence: 58%

“…Patient's clinical data included: age, AF duration, LA area, LVEF. As in Lankveld et al ( 2016b ); Zeemering et al ( 2017 ), multivariate prediction models combining clinical data ( F CLIN ) and each of the retained signal complexity parameters ( F CLIN + SIG ) were built by logistic regression (LR); 15-fold cross validation was performed in order to get an unbiased evaluation of a model fit on the training dataset. The output model was then applied to the validation set to determine the LR probability estimates and assign them to the related category.…”

Section: Methodsmentioning

confidence: 99%

“…Spectral measures of spatiotemporal organization computed from surface ECG were able to discriminate between persistent and long-standing AF (Uldry et al, 2012 ). Atrial complexity indices from ECG could also predict sinus rhythm (SR) maintenance in patients undergoing electrical cardioversion, either alone (Lankveld et al, 2016a ) or in combination with other clinical parameters (Zeemering et al, 2017 ). An optimal set of ECG descriptors of AF complexity has also been determined in Lankveld et al ( 2016b ), and it was shown to be predictive of CA outcome.…”

Section: Introductionmentioning

confidence: 99%

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Noninvasive Assessment of Atrial Fibrillation Complexity in Relation to Ablation Characteristics and Outcome

et al. 2018

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Background: The use of surface recordings to assess atrial fibrillation (AF) complexity is still limited in clinical practice. We propose a noninvasive tool to quantify AF complexity from body surface potential maps (BSPMs) that could be used to choose patients who are eligible for AF ablation and assess therapy impact.Methods: BSPMs (mean duration: 7 ± 4 s) were recorded with a 252-lead vest in 97 persistent AF patients (80 male, 64 ± 11 years, duration 9.6 ± 10.4 months) before undergoing catheter ablation. Baseline cycle length (CL) was measured in the left atrial appendage. The procedural endpoint was AF termination. The ablation strategy impact was defined in terms of number of regions ablated, radiofrequency delivery time to achieve AF termination, and acute outcome. The atrial fibrillatory wave signal extracted from BSPMs was divided in 0.5-s consecutive segments, each projected on a 3D subspace determined through principal component analysis (PCA) in the current frame. We introduced the nondipolar component index (NDI) that quantifies the fraction of energy retained after subtracting an equivalent PCA dipolar approximation of heart electrical activity. AF complexity was assessed by the NDI averaged over the entire recording and compared to ablation strategy.Results: AF terminated in 77 patients (79%), whose baseline AF CL was 177 ± 40 ms, whereas it was 157 ± 26 ms in patients with unsuccessful ablation outcome (p = 0.0586). Mean radiofrequency emission duration was 35 ± 21 min; 4 ± 2 regions were targeted. Long-lasting AF patients (≥12 months) exhibited higher complexity, with higher NDI values (≥12 months: 0.12 ± 0.04 vs. <12 months: 0.09 ± 0.03, p < 0.01) and short CLs (<160 ms: 0.12 ± 0.03 vs. between 160 and 180 ms: 0.10 ± 0.03 vs. >180 ms: 0.09 ± 0.03, p < 0.01). More organized AF as measured by lower NDI was associated with successful ablation outcome (termination: 0.10 ± 0.03 vs. no termination: 0.12 ± 0.04, p < 0.01), shorter procedures (<30 min: 0.09 ± 0.04 vs. ≥30 min: 0.11 ± 0.03, p < 0.001) and fewer ablation targets (<4: 0.09 ± 0.03 vs. ≥4: 0.11 ± 0.04, p < 0.01).Conclusions: AF complexity can be noninvasively quantified by PCA in BSPMs and correlates with ablation outcome and AF pathophysiology.

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