Andina Mario E, Nelde Alexander, Nolte Christian H, Scheitz Jan F, Olma Manuel C, Krämer Michael, Meisel Eckhard, Bingel Anne, Meisel Andreas, Scheibe Franziska, Endres Matthias, Schlemm Ludwig, Meisel Christian
Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Center for Stroke Research Berlin, Berlin, Germany.
Heliyon. 2023 Jul 19;9(8):e18432. doi: 10.1016/j.heliyon.2023.e18432. eCollection 2023 Aug.
(1) To assess the accuracy of a standard operating procedure (SOP) regarding the utilization of atrial fibrillation (AF) alarms in everyday clinical practice, and (2) to evaluate the performance of automated continuous surveillance for atrial fibrillation (AF) in hospitalized acute stroke patients.
Retrospective cohort study.
Two stroke units from two tertiary care hospitals in Berlin, Germany.
We identified 635 patients with ischemic stroke diagnosis for the time period between 01. January and 30. September 2021 of which 176 patients had recorded AF alarms during monitoring. Of those, 115 patients were randomly selected for evaluation. After excluding 6 patients with hemorrhagic stroke in their records, 109 patients (mean age: 79.1 years, median NIHSS at admission: 6, 57% female) remained for analysis.
Using a clinical data warehouse for comprehensive data storage we retrospectively downloaded and visualized ECG data segments of 65 s duration around the automated AF alarms. We restricted the maximum number of ECG segments to ten per patient. Each ECG segment plot was uploaded into a REDCap database and categorized as either AF, non-AF or artifact by manual review. Atrial flutter was subsumed as AF. These classifications were then matched with 1) medical history and known diseases before stroke, 2) discharge diagnosis, and 3) recommended treatment plan in the medical history using electronic health records.
The primary outcome was the proportion of previously unknown AF diagnoses correctly identified by the monitoring system but missed by the clinical team during hospitalization. Secondary outcomes included the proportion of patients in whom a diagnosis of AF would likely have led to anticoagulant therapy. We also evaluated the accuracy of the automated detection system in terms of its positive predictive value (PPV).
We evaluated a total of 717 ECG alarm segments from 109 patients. In 4 patients (3.7, 95% confidence interval [CI] 1.18-9.68%) physicians had missed AF despite at least one true positive alarm. All four patients did not receive long-term secondary prevention in form of anticoagulant therapy. 427 out of 717 alarms were rated true positives, resulting in a positive predictive value of 0.6 (CI 0.56-0.63) in this cohort.
By connecting a data warehouse, electronic health records and a REDCap survey tool, we introduce a path to assess the monitoring quality of AF in acute stroke patients. We find that implemented standards of procedure to detect AF during stroke unit care are effective but leave room for improvement. Such data warehouse-based concepts may help to adjust internal processes or identify targets of further investigations.
（1）评估一项关于在日常临床实践中使用房颤（AF）警报的标准操作程序（SOP）的准确性，以及（2）评估住院急性卒中患者房颤自动连续监测的性能。
回顾性队列研究。
德国柏林两家三级护理医院的两个卒中单元。
我们确定了2021年1月1日至9月30日期间635例缺血性卒中诊断患者，其中176例在监测期间记录到房颤警报。在这些患者中，随机选择115例进行评估。排除记录中有6例出血性卒中患者后，109例患者（平均年龄：79.1岁，入院时美国国立卫生研究院卒中量表（NIHSS）中位数：6，57%为女性）留作分析。
使用临床数据仓库进行全面数据存储，我们回顾性下载并可视化了自动房颤警报前后65秒时长的心电图数据段。我们将每位患者的心电图段最大数量限制为10个。每个心电图段图上传到一个REDCap数据库，并通过人工审核分类为房颤、非房颤或伪迹。心房扑动归为房颤。然后将这些分类与1）卒中前病史和已知疾病、2）出院诊断以及3）病史中推荐的治疗方案使用电子健康记录进行匹配。
主要结局是监测系统正确识别但临床团队在住院期间遗漏的既往未知房颤诊断的比例。次要结局包括房颤诊断可能导致抗凝治疗的患者比例。我们还根据阳性预测值（PPV）评估了自动检测系统的准确性。
我们评估了109例患者的总共717个心电图警报段。在4例患者（3.7%，95%置信区间[CI]1.18 - 9.68%）中，尽管至少有一次真阳性警报，医生仍遗漏了房颤。所有4例患者均未接受抗凝治疗形式的长期二级预防。717个警报中有427个被评为真阳性，该队列的阳性预测值为0.6（CI 0.56 - 0.63）。
通过连接数据仓库、电子健康记录和REDCap调查工具，我们引入了一条评估急性卒中患者房颤监测质量的途径。我们发现，在卒中单元护理期间实施的检测房颤的程序标准是有效的，但仍有改进空间。这种基于数据仓库的概念可能有助于调整内部流程或确定进一步研究的目标。
