Hennings Elisa, Coslovsky Michael, Paladini Rebecca E, Aeschbacher Stefanie, Knecht Sven, Schlageter Vincent, Krisai Philipp, Badertscher Patrick, Sticherling Christian, Osswald Stefan, Kühne Michael, Zuern Christine S
Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland.
Cardiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Cardiovasc Digit Health J. 2023 Jan 27;4(2):41-47. doi: 10.1016/j.cvdhj.2023.01.003. eCollection 2023 Apr.
Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-based tool could facilitate the assessment of AF burden.
We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool.
We analyzed 7-day Holter electrocardiogram (ECG) recordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agreement between both techniques by means of Pearson correlation coefficient, linear regression model, and Bland-Altman plot.
We assessed the AF burden in 100 Holter ECG recordings of 82 patients. We identified 53 Holter ECGs with 0% or 100% AF burden, where we found a 100% correlation. For the remaining 47 Holter ECGs with an AF burden between 0.01% and 81.53%, Pearson correlation coefficient was 0.998. The calibration intercept was -0.001 (95% CI -0.008; 0.006), and the calibration slope was 0.975 (95% CI 0.954; 0.995; multiple R 0.995, residual standard error 0.017). Bland-Altman analysis resulted in a bias of -0.006 (95% limits of agreement -0.042 to 0.030).
The assessment of AF burden with an AI-based tool provided very similar results compared to manual assessment. An AI-based tool may therefore be an accurate and efficient option for the assessment of AF burden.
新出现的证据表明，高房颤（AF）负荷与不良结局相关。然而，临床实践中并未常规测量房颤负荷。基于人工智能（AI）的工具可能有助于房颤负荷的评估。
我们旨在比较医生手动评估房颤负荷与基于人工智能的工具测量房颤负荷的情况。
我们分析了纳入前瞻性多中心瑞士房颤负荷队列研究的房颤患者的7天动态心电图（ECG）记录。房颤负荷定义为房颤持续时间的百分比，由医生和基于人工智能的工具（波兰克拉科夫的Cardiomatics）进行手动评估。我们通过Pearson相关系数、线性回归模型和Bland-Altman图评估了两种技术之间的一致性。
我们评估了82例患者的100份动态心电图记录中的房颤负荷。我们识别出53份房颤负荷为0%或100%的动态心电图，发现其相关性为100%。对于其余47份房颤负荷在0.01%至81.53%之间的动态心电图，Pearson相关系数为0.998。校准截距为-0.001（95%可信区间-0.008；0.006），校准斜率为0.975（95%可信区间0.954；0.995；复相关系数0.995，残差标准误0.017）。Bland-Altman分析得出的偏倚为-0.006（95%一致性界限-0.042至0.030）。
与手动评估相比，基于人工智能的工具评估房颤负荷提供了非常相似的结果。因此，基于人工智能的工具可能是评估房颤负荷的准确且高效的选择。
