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- Anselmino, Matteo, et al.
(författare)
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Atrial fibrillation ablation long-term ESC-EHRA EORP AFA LT registry : in-hospital and 1-year follow-up findings in Italy
- 2020
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Ingår i: Journal of Cardiovascular Medicine. - : Ovid Technologies (Wolters Kluwer Health). - 1558-2027 .- 1558-2035. ; 21:10, s. 740-748
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To report the Italian data deriving from the European Society of Cardiology-EURObservational Research Program atrial fibrillation ablation long-term registry.Methods and results: Ten Italian centers enrolled up to 50 consecutive patients undergoing atrial fibrillation ablation. Of the 318 patients included, 5 (1.6%) did not undergo catheter ablation, 1 had ablation partially done and 62 were lost at 1-year follow-up. Women were less represented (23.6%) and the median age was 60.0 years. A total of 195 patients (62.3%) suffered paroxysmal atrial fibrillation, whereas only 9 (2.9%) had long-standing persistent atrial fibrillation. Most Italian patients (92.3%) were symptomatic but suffering fewer symptomatic events than patients enrolled in other countries (median of two events in the month preceding the ablation vs. three, respectively; P<0.0001). The main finding of the study is that the success rate at 1 year, with and without antiarrhythmic drugs, was 76.4%, consistently with other participating countries (73.4%). This result was obtained however, with a significantly lower prevalence of 1-year adverse events (7.3 vs. 16.6%, P<0.0001). Procedure duration and fluoroscopy total time resulted as being shorter in Italy (145 vs. 160, P=0.0005 and 16.9 vs. 20.0 min, P=0.0018, respectively); however, the radiation dose per BSA was greater (37.5 vs. 26.0mGy/cm(2), P=0.0022).Conclusion: The demographic characteristics of patients undergoing atrial fibrillation ablation are similar to those reported in other countries. The success rate in Italy is consistent with those in other countries, whereas the complications rate is lower.
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| 2. |
- Saglietto, Andrea, et al.
(författare)
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AFA-Recur : an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation
- 2023
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Ingår i: Europace. - : Oxford University Press. - 1099-5129 .- 1532-2092. ; 25:1, s. 92-100
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Tidskriftsartikel (refereegranskat)abstract
- Aims: Atrial fibrillation (AF) recurrence during the first year after catheter ablation remains common. Patient-specific prediction of arrhythmic recurrence would improve patient selection, and, potentially, avoid futile interventions. Available prediction algorithms, however, achieve unsatisfactory performance. Aim of the present study was to derive from ESC-EHRA Atrial Fibrillation Ablation Long-Term Registry (AFA-LT) a machine-learning scoring system based on pre-procedural, easily accessible clinical variables to predict the probability of 1-year arrhythmic recurrence after catheter ablation.Methods and results: Patients were randomly split into a training (80%) and a testing cohort (20%). Four different supervised machine-learning models (decision tree, random forest, AdaBoost, and k-nearest neighbour) were developed on the training cohort and hyperparameters were tuned using 10-fold cross validation. The model with the best discriminative performance on the testing cohort (area under the curve-AUC) was selected and underwent further optimization, including re-calibration. A total of 3128 patients were included. The random forest model showed the best performance on the testing cohort; a 19-variable version achieved good discriminative performance [AUC 0.721, 95% confidence interval (CI) 0.680-0.764], outperforming existing scores (e.g. APPLE score: AUC 0.557, 95% CI 0.506-0.607). Platt scaling was used to calibrate the model. The final calibrated model was implemented in a web calculator, freely available at http://afarec.hpc4ai.unito.ti/.Conclusion: AFA-Recur, a machine-learning-based probability score predicting 1-year risk of recurrent atrial arrhythmia after AF ablation, achieved good predictive performance, significantly better than currently available tools. The calculator, freely available online, allows patient-specific predictions, favouring tailored therapeutic approaches for the individual patient.
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