Lu Mei, Chacra Wadih, Rabin David, Rupp Loralee B, Trudeau Sheri, Li Jia, Gordon Stuart C
Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA.
Division of Gastroenterology and Hepatology, University of Illinois College of Medicine, Chicago, IL, USA.
Clin Epidemiol. 2017 Jul 12;9:369-376. doi: 10.2147/CLEP.S136134. eCollection 2017.
Viral hepatitis-induced cirrhosis can progress to decompensated cirrhosis. Clinical decompensation represents a milestone event for the patient with cirrhosis, yet there remains uncertainty regarding precisely how to define this important phenomenon. With the development of broader treatment options for cirrhotic hepatitis patients, efficient identification of liver status before evolving to decompensated cirrhosis could be life-saving, but research on the topic has been limited by inconsistencies across studies, populations, and case-confirmation methods. We sought to determine whether diagnosis/procedure codes drawn from electronic health records (EHRs) could be used to identify patients with decompensated cirrhosis. In our first step, chart review was used to determine liver status (compensated cirrhosis, decompensated cirrhosis, non-cirrhotic) in patients from the Chronic Hepatitis Cohort Study. Next, a hybrid approach between Least Absolute Shrinkage and Selection Operator regression and Classification Regression Trees models was used to optimize EHR-based identification of decompensated cirrhosis, based on 41 diagnosis and procedure codes. These models were validated using tenfold cross-validation; method accuracy was evaluated by positive predictive values (PPVs) and area under receiver operating characteristic (AUROC) curves. Among 296 patients (23 with hepatitis B, 268 with hepatitis C, and 5 co-infected) with a 2:1 ratio of biopsy-confirmed cirrhosis to noncirrhosis, chart review identified 127 cases of decompensated cirrhosis (Kappa=0.88). The algorithm of five liver-related conditions-liver transplant, hepatocellular carcinoma, esophageal varices complications/procedures, ascites, and cirrhosis-yielded a PPV of 85% and an AUROC of 92%. A hierarchical subset of three conditions (hepatocellular carcinoma, ascites, and esophageal varices) demonstrated a PPV of 81% and an AUROC of 86%. Given the excellent predictive ability of our model, this EHR-based automated algorithm may be used to successfully identify patients with decompensated cirrhosis. This algorithm may contribute to timely identification and treatment of viral hepatitis patients who have progressed to decompensated cirrhosis.
病毒性肝炎所致肝硬化可进展为失代偿期肝硬化。临床失代偿是肝硬化患者的一个里程碑事件，但对于如何精确界定这一重要现象仍存在不确定性。随着针对肝硬化性肝炎患者的治疗选择不断增多，在发展为失代偿期肝硬化之前有效识别肝脏状态可能会挽救生命，但该主题的研究受到研究、人群和病例确认方法不一致的限制。我们试图确定从电子健康记录（EHR）中提取的诊断/程序代码是否可用于识别失代偿期肝硬化患者。在第一步中，通过病历审查来确定慢性丙型肝炎队列研究中患者的肝脏状态（代偿期肝硬化、失代偿期肝硬化、非肝硬化）。接下来，基于41个诊断和程序代码，采用最小绝对收缩和选择算子回归与分类回归树模型之间的混合方法，以优化基于EHR的失代偿期肝硬化识别。这些模型使用十折交叉验证进行验证；通过阳性预测值（PPV）和受试者操作特征曲线下面积（AUROC）评估方法准确性。在296例患者（23例乙型肝炎、268例丙型肝炎和5例合并感染）中，活检确诊的肝硬化与非肝硬化比例为2:1，病历审查确定了127例失代偿期肝硬化病例（Kappa=0.88）。由肝脏移植、肝细胞癌、食管静脉曲张并发症/手术、腹水和肝硬化这五种肝脏相关疾病组成的算法，其PPV为85%，AUROC为92%。由肝细胞癌、腹水和食管静脉曲张这三种疾病组成的分层子集，其PPV为81%，AUROC为86%。鉴于我们模型具有出色的预测能力，这种基于EHR的自动化算法可用于成功识别失代偿期肝硬化患者。该算法可能有助于及时识别和治疗已进展为失代偿期肝硬化的病毒性肝炎患者。
