背景：隐源性卒中后，患者通常需要长时间的心脏监测；然而，从长期心律监测获益的患者亚组没有明确定义.
目标：使用年龄，性别,合并症,基线12导联心电图，短期心律监测和超声心动图数据，我们创建了风险评分,并将其与之前发布的风险评分进行了比较.
方法：回顾性分析了2017年5月至2022年6月在蒙特菲奥雷医学中心接受的初次诊断为隐源性卒中或TIA的患者，这些患者接受了植入式心脏监护仪的长期节律监测。
结果：与有临床意义的心房颤动诊断呈正相关的变量包括年龄（p<0.001），种族（p=0.022），糖尿病状态（p=0.026），和COPD状态（p=0.012），心房运行的存在（p=0.003），每24小时心房运行次数（p<0.001），每24小时的心房运行搏动总数（p<0.001）和最长心房运行中的搏动数（p<0.001），LA增大(p=0.007)和至少轻度二尖瓣反流(p=0.009)。我们为我们的人群创建了一个风险分层评分，称为“ACL得分”。“ACL评分显示优于CHA2DS2-VASc评分，并且与C2HEST评分在预测设备检测到的AF方面具有可比性。
结论：ACL评分使临床医生能够更好地预测哪些患者在隐源性卒中后更有可能被诊断为设备检测到的房颤。
BACKGROUND: After a cryptogenic stroke, patients often will require prolonged cardiac monitoring; however, the subset of patients who would benefit from long-term rhythm monitoring is not clearly defined.
OBJECTIVE: The purpose of this study was to create a risk score by identifying significant predictors of atrial fibrillation (AF) using age, sex, comorbidities, baseline 12-lead electrocardiogram, short-term rhythm monitoring, and echocardiographic data and to compare it to previously published risk scores.
METHODS: Patients admitted to Montefiore Medical Center between May 2017 and June 2022 with a primary diagnosis of cryptogenic stroke or transient ischemic attack who underwent long-term rhythm monitoring with an implantable cardiac monitor were retrospectively analyzed.
RESULTS: Variables positively associated with a diagnosis of clinically significant AF include age (P <.001), race (P = .022), diabetes status (P = .026), chronic obstructive pulmonary disease status (P = .012), presence of atrial runs (P = .003), number of atrial runs per 24 hours (P <.001), total number of atrial run beats per 24 hours (P <.001), number of beats in the longest atrial run (P <.001), left atrial enlargement (P = .007), and at least mild mitral regurgitation (P = .009). We created a risk stratification score for our population, termed the ACL score. The ACL score demonstrated superiority to the CHA2DS2-VASc score and comparability to the C2HEST score for predicting device-detected AF.
CONCLUSIONS: The ACL score enables clinicians to better predict which patients are more likely to be diagnosed with device-detected AF after a cryptogenic stroke.
