作者
Konrad Pieszko, Jarosław Hiczkiewicz, Katarzyna Łojewska, Beata Uziębło-Życzkowska, Paweł Krzesiński, Monika Gawałko, Monika Budnik, Katarzyna Starzyk, Beata Wożakowska-Kapłon, Ludmiła Daniłowicz-Szymanowicz, Damian Kaufmann, Maciej Wójcik, Robert Błaszczyk, Katarzyna Mizia-Stec, Maciej Wybraniec, Katarzyna Kosmalska, Marcin Fijałkowski, Anna Szymańska, Mirosław Dłużniewski, Michał Kucio, Maciej Haberka, Karolina Kupczyńska, Błażej Michalski, Anna Tomaszuk-Kazberuk, Katarzyna Wilk-Śledziewska, Renata Wachnicka-Truty, Marek Koziński, Jacek Kwieciński, Rafał Wolny, Ewa Kowalik, Iga Kolasa, Agnieszka Jurek, Jan Budzianowski, Paweł Burchardt, Agnieszka Kapłon-Cieślicka, Piotr J Slomka
发表日期
2024/1/1
期刊
European Heart Journal
卷号
45
期号
1
页码范围
32-41
出版商
Oxford University Press
简介
Aims
Transoesophageal echocardiography (TOE) is often performed before catheter ablation or cardioversion to rule out the presence of left atrial appendage thrombus (LAT) in patients on chronic oral anticoagulation (OAC), despite associated discomfort. A machine learning model [LAT-artificial intelligence (AI)] was developed to predict the presence of LAT based on clinical and transthoracic echocardiography (TTE) features.
Methods and results
Data from a 13-site prospective registry of patients who underwent TOE before cardioversion or catheter ablation were used. LAT-AI was trained to predict LAT using data from 12 sites (n = 2827) and tested externally in patients on chronic OAC from two sites (n = 1284). Areas under the receiver operating characteristic curve (AUC) of LAT-AI were compared with that of left ventricular ejection fraction (LVEF) and CHA2DS2-VASc score …
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K Pieszko, J Hiczkiewicz, K Łojewska… - European Heart Journal, 2024