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- Jelena Kornej, Petra Büttner, Philipp Sommer, Nikolaos Dagres, Borislav Dinov, Katja Schumacher, Andreas Bollmann, and Gerhard Hindricks.
- Department of Electrophysiology, Heart Center, HELIOS Kliniken GmbH, Struempellstrasse 39, Leipzig, Germany.
- Europace. 2019 Jan 1; 21 (1): 54-59.
AimsLow voltage areas (LVAs) represent advanced remodelling processes in left atrium in patients with atrial fibrillation (AF) and are associated with higher rates of arrhythmia recurrences. However, the prediction of LVA based on clinical parameters is understudied. Recently, we introduced APPLE score to predict rhythm outcomes after catheter ablation. The aim of this study was to analyse (i) LVA prediction using APPLE score and (ii) differences in biomarker profiles according to APPLE score in AF patients.Methods And ResultsPatients undergoing first AF ablation were included. The APPLE score (one point for Age >65 years, Persistent AF, imPaired eGFR <60 mL/min/1.73 m2, LA diameter ≥43 mm, EF <50%) was calculated before ablation. Blood plasma samples from femoral vein were collected before ablation. Low voltage area were determined using high-density maps and defined as <0.5 mV. NT-proANP, NT-proBNP, L-Selectin, and vascular cell adhesion protein 1 (VCAM-1) were studied using commercially available assays. We studied 214 patients [age median (interquartile range) 65 (57-72) years, 59% males, 59% persistent AF, 27% LVA]. There were 42% patients with APPLE ≥3. The levels of NT-proANP (P < 0.001), NT-proBNP (P = 0.016), and VCAM-1 (P = 0.040) increased with each APPLE point. In the univariable analysis, APPLE score [odds ratio (OR) 1.921, 95% confidence interval (CI) 1.453-2.538; P < 0.001], female gender (OR 2.283, 95% CI 1.280-4.071; P = 0.005), and NT-proANP (OR 1.031, 95% CI 1.008-1.054; P = 0.007) were significant predictors for LVA. On the multivariable analysis, only APPLE score and female gender remained associated with LVA.ConclusionThe APPLE score can be used for prediction of LVA before AF ablation. There was a positive correlation between biomarker levels and APPLE score.
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