Ross Linda Sarah, Bettin Markus, Kochhäuser Simon, Ritter Martin, Minnerup Jens, Eckardt Lars, Reinke Florian, Dittrich Ralf
Department of Neurology, University Hospital of Muenster, Muenster, Germany.
Division of Electrophysiology, Department of Cardiovascular Medicine, University Hospital of Muenster, Muenster, Germany.
Cerebrovasc Dis. 2018;45(1-2):54-60. doi: 10.1159/000485193. Epub 2018 Jan 17.
Atrial fibrillation (AF) is an important cause of stroke. Continuous electrocardiography (ECG) monitoring with software-based analysis algorithms has been suggested to enhance the AF detection rate. We investigated the ability of stroke risk analysis (SRA) in the detection of AF in acute stroke patients.
Consecutive stroke patients numbering 1,153 were screened. Patients with cardioembolic stroke related to AF (n = 296, paroxysmal n = 63, persistent n = 233) and patients with cryptogenic stroke (n = 309) after standard diagnostic work-up (bedside ECG monitoring, ultrasound, transesophageal echocardiography, 24 h Holter ECG) received SRA during their stay at the Stroke Unit. Determination of AF risk by SRA in the patients with AF and in the patient group with cryptogenic stroke was assessed and compared.
Median SRA monitoring analysis time was 16 h (range 2-206 h, interquartile range 10-36). In AF patients, SRA also detected a possible or definitive AF in 98%. The overall sensitivity of SRA to detect possible or definitive AF in patients with proven AF by standard diagnostic work up and cryptogenic stroke was 98%, specificity 27%, positive predictive value 56%, and the negative predictive value (NPV) was 92%. Area under ROC curve was 0.622.
SRA was found to be highly sensitive to detect possible or definitive AF in clinical routine within a short monitoring time. However, low specificity and poor accuracy do not allow diagnosing AF by SRA alone, but with the high NPV compared to current diagnostic standard, it is a valid diagnostic tool to rule out AF. Thereby, SRA is a contribution to clarify stroke etiology.
心房颤动（AF）是卒中的一个重要病因。有人提出采用基于软件分析算法的连续心电图（ECG）监测来提高AF的检测率。我们研究了卒中风险分析（SRA）在急性卒中患者中检测AF的能力。
对1153例连续的卒中患者进行筛查。在标准诊断检查（床边ECG监测、超声、经食管超声心动图、24小时动态心电图）后，患有与AF相关的心源性栓塞性卒中的患者（n = 296，阵发性n = 63，持续性n = 233）和隐源性卒中患者（n = 309）在卒中单元住院期间接受SRA。评估并比较SRA在AF患者和隐源性卒中患者组中对AF风险的判定。
SRA监测分析的中位时间为16小时（范围2 - 206小时，四分位间距10 - 36）。在AF患者中，SRA也在98%的患者中检测到可能或确诊的AF。SRA在经标准诊断检查确诊为AF的患者和隐源性卒中患者中检测可能或确诊AF的总体敏感性为98%，特异性为27%，阳性预测值为56%，阴性预测值（NPV）为92%。ROC曲线下面积为0.622。
发现SRA在短时间监测内对临床常规中检测可能或确诊的AF高度敏感。然而，低特异性和低准确性不允许仅通过SRA诊断AF，但与当前诊断标准相比NPV高，它是排除AF的有效诊断工具。因此，SRA有助于明确卒中病因。
