AFA-Recur : an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023001%3A_____%2F23%3A00083838" target="_blank" >RIV/00023001:_____/23:00083838 - isvavai.cz</a>
Result on the web
<a href="https://academic.oup.com/europace/article/25/1/92/6675609?login=true#401964376" target="_blank" >https://academic.oup.com/europace/article/25/1/92/6675609?login=true#401964376</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/europace/euac145" target="_blank" >10.1093/europace/euac145</a>
Alternative languages
Result language
angličtina
Original language name
AFA-Recur : an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation
Original language description
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. [GRAPHICS] .
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30201 - Cardiac and Cardiovascular systems
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Europace
ISSN
1099-5129
e-ISSN
1532-2092
Volume of the periodical
25
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
92-100
UT code for WoS article
000844326500001
EID of the result in the Scopus database
2-s2.0-85147783591