Khan Mohammad Qasim, Anand Vijay, Hessefort Norbert, Hassan Ammar, Ahsan Alya, Sonnenberg Amnon, Fimmel Claus J
North Shore University Health System, Evanston, IL, USA.
Portland VA Medical Center and the Oregon Health & Science University, Portland, OR, USA.
J Transl Int Med. 2017 Mar 31;5(1):43-48. doi: 10.1515/jtim-2017-0011. eCollection 2017 Mar.
To determine whether advanced cirrhosis - defined by the detection of nodular liver contours or portal venous collaterals on imaging studies - could be predicted by fibrosis algorithms, calculated using laboratory and demographic features extracted from patients' electronic medical records. To this end, we compared seven EMR-based fibrosis scores with liver imaging studies in a cohort of HCV patients.
A search of our health system's patient data warehouse identified 867 patients with chronic HCV infection. A total of 565 patients had undergone at least one liver imaging study and had no confounding medical condition affecting the imaging features or fibrosis scores. Demographic and laboratory data were used to calculate APRI, Fib4, Fibrosis Index, Forns, GUCI, Lok Index and Vira-HepC scores for all viremic patients who had undergone liver imaging. Data points selected for the calculation of these scores were based on laboratory results obtained within the shortest possible time from the imaging study. Areas under the receiver operating curves (AUROC), optimum cut-offs, sensitivities, specificities and positive and negative predictive values were calculated for each score.
Seven algorithms were performed similarly in predicting cirrhosis. Sensitivities ranged from 0.65 to 1.00, specificities from 0.67 to 0.90, positive predictive values from 0.33 to 0.38, and negative predictive values from 0.93 to 1.00. No individual test was superior, as the confidence intervals of all AUROCs overlapped.
EMR-based scoring systems performed relatively well in ruling out advanced, radiologically-defined cirrhosis. However, their moderate sensitivity and positive predictive values limit their reliability for EMR-based diagnosis.
确定通过纤维化算法能否预测晚期肝硬化（通过影像学检查发现肝脏轮廓结节或门静脉侧支循环来定义），该算法使用从患者电子病历中提取的实验室和人口统计学特征进行计算。为此，我们在一组丙型肝炎病毒（HCV）患者中，将七种基于电子病历的纤维化评分与肝脏影像学检查进行了比较。
在我们医疗系统的患者数据仓库中进行搜索，确定了867例慢性HCV感染患者。共有565例患者接受了至少一次肝脏影像学检查，且没有影响影像学特征或纤维化评分的混杂疾病。对于所有接受过肝脏影像学检查的病毒血症患者，使用人口统计学和实验室数据来计算天冬氨酸氨基转移酶与血小板比值指数（APRI）、Fib4、纤维化指数、福恩斯指数（Forns）、全球大学合作指数（GUCI）、洛克指数（Lok Index）和Vira-HepC评分。计算这些评分所选的数据点基于在尽可能短的时间内从影像学检查中获得的实验室结果。计算每个评分的受试者工作特征曲线下面积（AUROC）、最佳截断值、敏感性、特异性以及阳性和阴性预测值。
七种算法在预测肝硬化方面表现相似。敏感性范围为0.65至1.00，特异性范围为0.67至0.90，阳性预测值范围为0.33至0.38，阴性预测值范围为从0.93至1.00。没有一种单独的检测方法更具优势，因为所有AUROC的置信区间相互重叠。
基于电子病历的评分系统在排除放射学定义的晚期肝硬化方面表现相对良好。然而，它们中等的敏感性和阳性预测值限制了其在基于电子病历的诊断中的可靠性。
