Reges Orna, Weinberg Hagay, Hoshen Moshe, Greenland Philip, Rayyan-Assi Hana'a, Avgil Tsadok Meytal, Bachrach Asaf, Balicer Ran, Leibowitz Morton, Haim Moti
Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Clin Epidemiol. 2020 May 20;12:477-483. doi: 10.2147/CLEP.S230677. eCollection 2020.
Previous studies have demonstrated differences in atrial fibrillation (AF) detection based on data from hospital sources without data from outpatient sources. We investigated the detection of documented diagnoses of non-valvular AF in a large Israeli health-care organization using electronic health record data from multiple sources.
This was an open-chart validation study. Three distinct algorithms for identifying AF in electronic health records, differing in the source of their International Classification of Diseases, Ninth Revision code and use of the associated free text, were defined. Algorithm 1 incorporated inpatient data with outpatient data and the associated free text. Algorithm 2 incorporated inpatient and outpatient data regardless of the free text associated with AF diagnosis. Algorithm 3 used only inpatient data source. These algorithms were compared to a gold standard and their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. To establish the gold standard (documentation of arrhythmia based on electrocardiography interpretation or a cardiologist's written diagnosis), 200 patients at highest risk for having non-valvular AF were randomly selected for open-chart validation by two physicians.
The algorithm that included hospital settings, outpatient settings, and incorporated associated free text in the outpatient records had the optimal balance between all validation measures, with a high level of sensitivity (85.4%), specificity (95.0%), PPV (81.4%), and NPV (96.2%). The alternative algorithm that combined inpatient and outpatient data without free text also performed better than the algorithm that included only hospital data (82.9%, 95.0%, 81.0%, and 95.6%, compared to 70.7%, 96.9%, 85.3%, and 92.8%, sensitivity, specificity, PPV, and NPV, respectively).
In this study, involving a comprehensive data collection from inpatient and outpatient sources, incorporating outpatient data with inpatient data improved the diagnosis of non-valvular AF compared to inpatient data alone.
以往研究表明,基于医院来源的数据而非门诊来源的数据,在心房颤动(AF)检测方面存在差异。我们使用来自多个来源的电子健康记录数据,对以色列一家大型医疗保健机构中记录在案的非瓣膜性AF诊断进行了调查。
这是一项开放图表验证研究。定义了三种在电子健康记录中识别AF的不同算法,它们在国际疾病分类第九版代码的来源以及相关自由文本的使用方面存在差异。算法1将住院患者数据与门诊患者数据以及相关自由文本相结合。算法2纳入住院和门诊数据,而不考虑与AF诊断相关的自由文本。算法3仅使用住院患者数据源。将这些算法与金标准进行比较,并计算它们的敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)。为了建立金标准(基于心电图解读或心脏病专家书面诊断的心律失常记录),随机选择200名非瓣膜性AF风险最高的患者,由两名医生进行开放图表验证。
将医院环境、门诊环境以及门诊记录中的相关自由文本相结合的算法,在所有验证指标之间具有最佳平衡,敏感性高(85.4%)、特异性高(95.0%)、PPV(81.4%)和NPV(96.2%)。不包含自由文本的住院和门诊数据组合的替代算法,也比仅包含医院数据的算法表现更好(敏感性、特异性、PPV和NPV分别为82.9%、95.0%、81.0%和95.6%,而仅包含医院数据的算法分别为70.7%、96.9%、85.3%和92.8%)。
在本研究中,涉及从住院和门诊来源进行全面数据收集,将门诊数据与住院数据相结合,与仅使用住院数据相比,改善了非瓣膜性AF的诊断。