Validation of a hierarchical algorithm to define chronic liver disease and cirrhosis etiology in administrative healthcare data
Validation of a hierarchical algorithm to define chronic liver disease and cirrhosis etiology in administrative healthcare data
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DOI：
10.1371/journal.pone.0229218
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发表时间：
2020-02-18
期刊：
影响因子：
3.7
通讯作者：
Flemming, Jennifer A.
中科院分区：
文献类型：
--
作者：
Philip, George;Djerboua, Maya;Flemming, Jennifer A.
背景和目的慢性肝病（CLD）和肝硬化是全球死亡的主要原因，在过去几十年中，疾病负担显着增加。定义肝病的病因对于了解肝病流行病学，医疗保健计划和结果非常重要。本研究的目的是验证CLD和肝硬化病因的层次算法在行政healthcare data. MethodsConcretive CLD或肝硬化患者参加门诊肝病诊所在安大略，加拿大从05/01/2013-08/31/2013进行了详细的图表抽象。金标准肝病病因由主治肝病学家确定为丙型肝炎（HCV）、乙型肝炎B（HBV）、酒精相关非酒精性脂肪性肝病（NAFLD）/隐源性、自身免疫性或血色素沉着症。个体数据与ICES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's agreement.Results442例患者接受了图表提取（中位年龄53岁，53%肝硬化，45%HCV，26%NAFLD，10%酒精相关），评估了结合实验室和管理代码定义病因的分层算法的诊断准确性。在肝硬化患者中，该算法对所有病因具有足够的灵敏度/PPV（> 75%）和极好的特异性/NPV（> 90%）。在那些没有肝硬化，该算法是优秀的所有病因，除了血色沉着病和自身免疫性diseases.ConclusionsA分层算法结合实验室和行政编码可以准确地定义肝硬化病因在常规收集的医疗保健数据。这些结果将促进在这一不断增长的患者群体的卫生服务研究。
Background and aimsChronic 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.MethodsConsecutive 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.Results442 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.ConclusionsA 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.