Viliani Dafne, Cecconi Alberto, Spinola Tena Miguel Angel, Vera Alberto, Ximenez-Carrillo Alvaro, Ramos Carmen, Martinez-Vives Pablo, Lopez-Melgar Beatriz, Montes Muniz Alvaro, Aguirre Clara, Vivancos Jose, Ortega Guillermo, Alfonso Fernando, Jimenez-Borreguero Luis Jesus
Cardiology Department, Ospedale Santa Chiara, Trento, Italy.
Cardiology Department, Hospital Universitario de La Princesa, Universidad Autonoma de Madrid, Madrid, Spain.
Cardiol Res. 2025 Apr;16(2):120-129. doi: 10.14740/cr2016. Epub 2025 Feb 6.
The detection of underlying paroxysmal atrial fibrillation (AF) in patients with cryptogenic stroke (CS) can be challenging, and there is great interest in finding predictors of its hidden presence. The recent development of sophisticated software has enhanced the diagnostic and prognostic performance of the 12-lead electrocardiogram (ECG). Our aim was to assess the additional role of a computer-assisted ECG analysis in identifying predictors of AF in patients with CS.
Sixty-seven patients with ischemic stroke or high-risk transient ischemic attack of unknown etiology were prospectively studied. Their 12-lead digitized ECG was analyzed with dedicated software, quantifying 468 morphological variables. The main clinical, biochemical, and echocardiographic variables were also collected. At discharge, patients were monitored with a wearable Holter for 15 days, and the primary outcome was the detection of AF.
The median age was 80 (interquartile range (IQR): 73 - 84) and AF was detected in 21 patients (31.3%). After preselecting significant ECG variables from the univariate analysis, a multivariate regression including other significant clinical, biochemical and echocardiographic predictors of AF was performed. Among the automatically analyzed ECG parameters, the amplitude of the R wave in V1 (V1_ramp) was significantly associated with the outcome. The best model to predict AF was composed of age, N-terminal B-type natriuretic peptide (NT-proBNP), left atrial reservoir strain (LASr) and V1_ramp. This model showed good discrimination capacity (corrected Somer's D: 0.907, Brier's B: 0.079, area under the curve (AUC): 0.941) and performed better than the same model without the ECG variable (Somer's D: 0.827, Brier's B: 0.119, AUC: 0.896).
The addition of computer-assisted ECG analysis can help stratify the risk of AF in the challenging clinical setting of CS.
在隐源性卒中（CS）患者中检测潜在的阵发性心房颤动（AF）具有挑战性，因此人们对寻找其隐匿存在的预测因素非常感兴趣。复杂软件的最新发展提高了12导联心电图（ECG）的诊断和预后性能。我们的目的是评估计算机辅助ECG分析在识别CS患者AF预测因素方面的额外作用。
对67例病因不明的缺血性卒中或高危短暂性脑缺血发作患者进行前瞻性研究。使用专用软件分析他们的12导联数字化ECG，量化468个形态学变量。还收集了主要的临床、生化和超声心动图变量。出院时，使用可穿戴式动态心电图监测患者15天，主要结局是检测到AF。
中位年龄为80岁（四分位间距（IQR）：73 - 84岁），21例患者（31.3%）检测到AF。在单因素分析中预先选择显著的ECG变量后，进行了包括AF其他显著临床、生化和超声心动图预测因素的多因素回归分析。在自动分析的ECG参数中，V1导联R波振幅（V1_ramp）与结局显著相关。预测AF的最佳模型由年龄、N末端B型利钠肽（NT-proBNP）、左心房储存应变（LASr）和V1_ramp组成。该模型显示出良好的辨别能力（校正的索默斯D：0.907，布赖尔B：0.079，曲线下面积（AUC）：0.941），并且比不包含ECG变量的相同模型表现更好（索默斯D：0.827，布赖尔B：0.119，AUC：0.896）。
在具有挑战性的CS临床环境中，添加计算机辅助ECG分析有助于对AF风险进行分层。
