Ge Jin, Najafi Nader, Zhao Wendi, Somsouk Ma, Fang Margaret, Lai Jennifer C
Division of Gastroenterology and HepatologyDepartment of MedicineUniversity of California San FranciscoSan FranciscoCAUSA.
Division of Hospital MedicineDepartment of MedicineUniversity of California San FranciscoSan FranciscoCAUSA.
Hepatol Commun. 2021 Mar 12;5(6):1069-1080. doi: 10.1002/hep4.1690. eCollection 2021 Jun.
Queries of electronic health record (EHR) data repositories allow for automated data collection. These techniques have not been used in hepatology due to the inability to capture hepatic encephalopathy (HE) grades, which are inputs for acute-on-chronic liver failure (ACLF) models. Here, we describe a methodology to use EHR data to calculate rolling ACLF scores. We examined 239 patient admissions with end-stage liver disease from July 2014 to June 2019. We mapped EHR flowsheet data to determine HE grades and calculated two longitudinally updated ACLF scores. We validated HE grades and ACLF diagnoses by chart review and calculated sensitivity, specificity, and Cohen's kappa. Of 239 patient admissions analyzed, 37% were women, 46% were non-Hispanic white, median age was 60 years, and the median Model for End-Stage Liver Disease-Na score at admission was 25. Of the 239, 7% were diagnosed with ACLF as defined by the North American Consortium for the Study of End-Stage Liver Disease (NACSELD) diagnostic criteria at admission, 27% during the hospitalization, and 9% at discharge. Forty percent were diagnosed with ACLF by the European Association for the Study of the Liver- Chronic Liver Failure Consortium (CLIF-C) diagnostic criteria at admission, 51% during the hospitalization, and 34% at discharge. From the chart review of 51 admissions, we found sensitivities and specificities for any HE (grades 1-4) were 92%-97% and 76%-95%, respectively; for severe HE (grades 3-4), sensitivities and specificities were 100% and 78%-98%, respectively. Cohen's kappa between flowsheet and chart review of HE grades ranged from 0.55 to 0.72. Sensitivities and specificities for NACSELD-ACLF diagnoses were 75%-100% and 96%-100%, respectively; for CLIF-C-ACLF diagnoses, these were 91%-100% and 96-100%, respectively. We generated approximately 28 unique ACLF scores per patient per admission day. We developed an informatics-based methodology to calculate longitudinally updated ACLF scores. This opens new analytic potentials, such as big data methods, to develop electronic phenotypes for patients with ACLF.
对电子健康记录（EHR）数据存储库进行查询可实现自动化数据收集。由于无法获取肝性脑病（HE）分级，而HE分级是慢性肝衰竭急性发作（ACLF）模型的输入参数，因此这些技术尚未在肝病学中使用。在此，我们描述一种使用EHR数据来计算动态ACLF评分的方法。我们研究了2014年7月至2019年6月期间239例终末期肝病患者的入院情况。我们对EHR流程表数据进行映射以确定HE分级，并计算了两个纵向更新的ACLF评分。我们通过病历审查验证了HE分级和ACLF诊断，并计算了敏感性、特异性和科恩kappa系数。在分析的239例患者入院情况中，37%为女性，46%为非西班牙裔白人，中位年龄为60岁，入院时终末期肝病钠评分模型的中位数为25。在这239例患者中，7%在入院时根据北美终末期肝病研究联盟（NACSELD）诊断标准被诊断为ACLF，27%在住院期间被诊断为ACLF，9%在出院时被诊断为ACLF。40%的患者根据欧洲肝脏研究协会-慢性肝衰竭联盟（CLIF-C）诊断标准在入院时被诊断为ACLF，51%在住院期间被诊断为ACLF，34%在出院时被诊断为ACLF。通过对51例入院病历的审查，我们发现任何HE（1 - 4级）的敏感性和特异性分别为92% - 97%和76% - 95%；对于严重HE（3 - 4级），敏感性和特异性分别为100%和78% - 98%。HE分级的流程表与病历审查之间的科恩kappa系数范围为0.55至0.72。NACSELD - ACLF诊断的敏感性和特异性分别为75% - 100%和96% - 100%；对于CLIF - C - ACLF诊断，敏感性和特异性分别为91% - 100%和96% - 100%。我们为每位患者的每个入院日生成了约28个独特的ACLF评分。我们开发了一种基于信息学的方法来计算纵向更新的ACLF评分。这开启了新的分析潜力，例如大数据方法，以开发ACLF患者的电子表型。
