Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Department of Cardiology II, University Medical Center of the Johannes Gutenberg University, Mainz, Germany.
Ann Clin Transl Neurol. 2020 Oct;7(10):1779-1787. doi: 10.1002/acn3.51157. Epub 2020 Aug 30.
Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice.
In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into "no risk of AF" or "risk of AF" and compared to clinical variables to predict AF during 72 hours Holter-ECG.
pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as "risk of AF" (n = 21, 17.8%) compared to "no risk of AF" (n = 33, 3.6%). AA-based risk stratification as "risk of AF" remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024-7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021-1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023-1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313-5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724-0.778; AUC for the combination: 0.789, 95% CI 0.763-0.814; difference between the AUC P = 0.022).
Automated software-based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.
在 72 小时 Holter ECG 中识别有发生阵发性心房颤动（pAF）风险的缺血性脑卒中患者，以便为个体化分配延长心电图监测时间提供依据，但目前该方法尚未常规应用于临床实践。
在一项前瞻性多中心研究中，对 1031 例急性缺血性卒中和 TIA 患者窦性心律 72 小时 Holter ECG 的最初可分析小时的原始心电图数据进行分析，由自动化软件（AA）将其分为“无 AF 风险”或“AF 风险”，并与临床变量进行比较，以预测 72 小时 Holter-ECG 期间的 AF。
54 例（5.2%；平均年龄 78 岁；女性 56%）患者诊断为 pAF，AA 分类为“AF 风险”（n=21，17.8%）的患者在 72 小时后更常检测到 pAF，而 AA 分类为“无 AF 风险”（n=33，3.6%）的患者则较少检测到。AA 为“AF 风险”的风险分层在预测 72 小时 Holter ECG 期间 pAF 检测方面仍然具有重要意义（OR3.814，95%CI 2.024-7.816，P<0.001），此外还有年龄（OR1.052，95%CI 1.021-1.084，P=0.001）、NIHSS（OR 1.087，95%CI 1.023-1.154，P=0.007）和溶栓治疗史（OR2.639，95%CI 1.313-5.306，P=0.006）。同样，与单纯的临床风险评分相比（AS5F 单独：AUC 0.751；95%CI 0.724-0.778；联合 AUC：0.789，95%CI 0.763-0.814；AUC 之间的差异 P=0.022），AA 进行的基于心电图的风险分层显著提高了预测 pAF 检测的接受者操作特征曲线（ROC）的曲线下面积（AUC）。
基于自动心电图软件的风险分层选择了 72 小时 Holter ECG 期间有发生 AF 风险的患者，并为 AF 预测的常见临床危险因素提供了预测价值。
