Translational Institute of Medicine, Queen's University, Kingston, Ontario, Canada.
ICES, Queen's University, Kingston, Ontario, Canada.
PLoS One. 2020 Feb 18;15(2):e0229218. doi: 10.1371/journal.pone.0229218. eCollection 2020.
Chronic liver disease (CLD) and cirrhosis are leading causes of death globally with the burden of disease rising significantly over the past several decades. Defining the etiology of liver disease is important for understanding liver disease epidemiology, healthcare planning, and outcomes. The aim of this study was to validate a hierarchical algorithm for CLD and cirrhosis etiology in administrative healthcare data.
Consecutive patients with CLD or cirrhosis attending an outpatient hepatology clinic in Ontario, Canada from 05/01/2013-08/31/2013 underwent detailed chart abstraction. Gold standard liver disease etiology was determined by an attending hepatologist as hepatitis C (HCV), hepatitis B (HBV), alcohol-related, non-alcoholic fatty liver disease (NAFLD)/cryptogenic, autoimmune or hemochromatosis. Individual data was linked to routinely collected administrative healthcare data at ICES. Diagnostic accuracy of a hierarchical algorithm incorporating both laboratory and administrative codes to define etiology was evaluated by calculating sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and kappa's agreement.
442 individuals underwent chart abstraction (median age 53 years, 53% cirrhosis, 45% HCV, 26% NAFLD, 10% alcohol-related). In patients with cirrhosis, the algorithm had adequate sensitivity/PPV (>75%) and excellent specificity/NPV (>90%) for all etiologies. In those without cirrhosis, the algorithm was excellent for all etiologies except for hemochromatosis and autoimmune diseases.
A hierarchical algorithm incorporating laboratory and administrative coding can accurately define cirrhosis etiology in routinely collected healthcare data. These results should facilitate health services research in this growing patient population.
慢性肝病（CLD）和肝硬化是全球主要的死亡原因，在过去几十年中，疾病负担显著增加。明确肝病的病因对于了解肝病的流行病学、医疗保健规划和结局非常重要。本研究的目的是验证一种用于管理医疗保健数据中 CLD 和肝硬化病因的分层算法。
2013 年 5 月 1 日至 8 月 31 日，在加拿大安大略省的一家门诊肝病诊所连续就诊的 CLD 或肝硬化患者接受了详细的病历摘录。由主治肝病专家确定金标准肝病病因，包括丙型肝炎（HCV）、乙型肝炎（HBV）、酒精相关、非酒精性脂肪性肝病（NAFLD）/隐匿性、自身免疫或血色病。个体数据通过安大略省临床评估科学研究所（ICES）与常规收集的管理医疗保健数据相关联。通过计算敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）以及 Kappa 的一致性来评估纳入实验室和管理代码来定义病因的分层算法的诊断准确性。
442 名患者接受了病历摘录（中位数年龄 53 岁，53%为肝硬化，45%为 HCV，26%为 NAFLD，10%为酒精相关）。在肝硬化患者中，该算法对于所有病因的敏感性/PPV（>75%）和特异性/NPV（>90%）均较高。在没有肝硬化的患者中，该算法除血色病和自身免疫性疾病外，对于所有病因均表现出色。
一种纳入实验室和管理编码的分层算法可以准确地定义常规收集的医疗保健数据中的肝硬化病因。这些结果应有助于在这个不断增长的患者群体中进行卫生服务研究。
