Department of Cardiology, University Hospital Heidelberg, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, University of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
Clin Res Cardiol. 2021 Jun;110(6):868-876. doi: 10.1007/s00392-020-01773-z. Epub 2020 Nov 19.
The early recognition of paroxysmal atrial fibrillation (pAF) is a major clinical challenge for preventing thromboembolic events. In this prospective and multicentric study we evaluated prediction scores for the presence of pAF, calculated from non-invasive medical history and echocardiographic parameters, in patients with unknown AF status.
The 12-parameter score with parameters age, LA diameter, aortic root diameter, LV,ESD, TDI A', heart frequency, sleep apnea, hyperlipidemia, type II diabetes, smoker, ß-blocker, catheter ablation, and the 4-parameter score with parameters age, LA diameter, aortic root diameter and TDI A' were tested. Presence of pAF was verified by continuous electrocardiogram (ECG) monitoring for up to 21 days in 305 patients.
The 12-parameter score correctly predicted pAF in all 34 patients, in which pAF was newly detected by ECG monitoring. The 12- and 4-parameter scores showed sensitivities of 100% and 82% (95%-CI 65%, 93%), specificities of 75% (95%-CI 70%, 80%) and 67% (95%-CI 61%, 73%), and areas under the receiver operating characteristic (ROC) curves of 0.84 (95%-CI 0.80, 0.88) and 0.81 (95%-CI 0.74, 0.87). Furthermore, properties of AF episodes and durations of ECG monitoring necessary to detect pAF were analysed.
The prediction scores adequately detected pAF using variables readily available during routine cardiac assessment and echocardiography. The model scores, denoted as ECHO-AF scores, represent simple, highly sensitive and non-invasive tools for detecting pAF that can be easily implemented in the clinical practice and might serve as screening test to initiate further diagnostic investigations for validating the presence of pAF. Prospective validation of a novel prediction model for paroxysmal atrial fibrillation based on echocardiography and medical history parameters by long-term Holter ECG.
阵发性心房颤动（pAF）的早期识别是预防血栓栓塞事件的主要临床挑战。在这项前瞻性的多中心研究中，我们评估了在未知 AF 状态的患者中，通过非侵入性的医学史和超声心动图参数计算的预测评分，用于存在 pAF 的预测。
该 12 个参数评分包括参数年龄、左心房直径、主动脉根部直径、左心室、ESD、TDI A'、心率、睡眠呼吸暂停、高血脂、2 型糖尿病、吸烟者、β受体阻滞剂、导管消融术，以及包括参数年龄、左心房直径、主动脉根部直径和 TDI A'的 4 个参数评分。通过在 305 例患者中进行长达 21 天的连续心电图（ECG）监测，来验证 pAF 的存在。
在通过 ECG 监测新发现的 34 例 pAF 患者中，12 个参数评分正确预测了 pAF。12 个和 4 个参数评分的敏感性分别为 100%和 82%（95%CI 65%，93%），特异性分别为 75%（95%CI 70%，80%）和 67%（95%CI 61%，73%），接受者操作特征（ROC）曲线下面积分别为 0.84（95%CI 0.80，0.88）和 0.81（95%CI 0.74，0.87）。此外，还分析了用于检测 pAF 的 AF 发作次数和 ECG 监测持续时间的特性。
预测评分使用在常规心脏评估和超声心动图中易于获得的变量，充分检测出了 pAF。模型评分，称为 ECHO-AF 评分，是一种简单、高度敏感且非侵入性的工具，用于检测 pAF，可轻松在临床实践中实施，并可作为筛查试验，以启动进一步的诊断研究，验证 pAF 的存在。基于超声心动图和医学史参数的阵发性心房颤动新预测模型的前瞻性验证，通过长期动态心电图。
