LoCoco Sarah, Kashou Anthony H, Deshmukh Abhishek J, Asirvatham Samuel J, DeSimone Christopher V, Mikhova Krasimira M, Sodhi Sandeep S, Cuculich Phillip S, Ghadban Rugheed, Cooper Daniel H, Maddox Thomas M, Noseworthy Peter A, May Adam M
Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, St. Louis, MO, United States.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States.
Data Brief. 2024 Dec 3;58:111198. doi: 10.1016/j.dib.2024.111198. eCollection 2025 Feb.
The differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia (SWCT) via 12-lead ECG (electrocardiogram) interpretation is a crucial yet demanding clinical task. Decades of research have been dedicated to simplifying and improving this differentiation via manual algorithms. Despite such research, the effectiveness of such algorithms still remains limited, primarily due to reliance on user expertise. To combat this limitation, automated algorithms have been created that show promise as alternatives to manual ECG interpretation. However, direct comparison of the methods' diagnostic performances has not been undertaken. A recent publication (LoCoco et al., 2024) compared the diagnostic performance between traditional manual ECG interpretation approaches (i.e. Brugada, Vereckei aVR, and VT Score) to novel automated wide QRS complex tachycardia differentiation algorithms (i.e. WCT Formula I, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). Two electrophysiologists independently applied the 3 manual WCT differentiation approaches to 213 ECGs. Simultaneously, computerized data from the same paired WCT with baseline ECGs were processed by the 5 automated WCT differentiation algorithms. Following these analyses, the diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. In this article, a summary of data components relating to diagnostic performance of the methods tested is presented.
通过12导联心电图（ECG）解读将宽QRS波群心动过速（WCTs）区分为室性心动过速（VT）和室上性宽QRS波群心动过速（SWCT）是一项至关重要但颇具挑战性的临床任务。数十年来，人们一直致力于通过人工算法简化和改进这种鉴别方法。尽管有此类研究，但这些算法的有效性仍然有限，主要原因是依赖用户的专业知识。为克服这一局限性，已创建了自动算法，显示出有望成为人工心电图解读的替代方法。然而，尚未对这些方法的诊断性能进行直接比较。最近的一篇出版物（洛科科等人，2024年）比较了传统人工心电图解读方法（即 Brugada法、韦雷克艾aVR法和室性心动过速评分法）与新型自动宽QRS波群心动过速鉴别算法（即WCT公式I、WCT公式II、室性心动过速预测模型、单独模型和配对模型）之间的诊断性能。两位电生理学家独立地将3种人工WCT鉴别方法应用于213份心电图。同时，5种自动WCT鉴别算法对来自同一配对WCT与基线心电图的计算机化数据进行了处理。经过这些分析，将自动算法的诊断性能与人工心电图解读方法进行了比较。在本文中，给出了与所测试方法的诊断性能相关的数据成分总结。
