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在卫生行政数据中识别肝硬化、失代偿性肝硬化和肝细胞癌:一项验证研究。

Identifying cirrhosis, decompensated cirrhosis and hepatocellular carcinoma in health administrative data: A validation study.

机构信息

Department of Medicine, University of Toronto, Toronto, Canada.

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.

出版信息

PLoS One. 2018 Aug 22;13(8):e0201120. doi: 10.1371/journal.pone.0201120. eCollection 2018.

Abstract

BACKGROUND

To evaluate screening and treatment strategies, large-scale real-world data on liver disease-related outcomes are needed. We sought to validate health administrative data for identification of cirrhosis, decompensated cirrhosis and hepatocellular carcinoma among patients with known liver disease.

METHODS

Primary patient data were abstracted from patients of the Toronto Center for Liver Disease with viral hepatitis (2006-2014), and all patients with liver disease from the Kingston Health Sciences Centre Hepatology Clinic (2013). We linked clinical information to health administrative data and tested a range of coding algorithms against the clinical reference standard.

RESULTS

A total of 6,714 patients had primary chart data abstracted. A single physician visit code for cirrhosis was sensitive (98-99%), and a single hospital diagnostic code for cirrhosis was specific (91-96%). The most sensitive algorithm for decompensated cirrhosis was one cirrhosis code with any of: a hospital diagnostic code, death code, or procedure code for decompensation (range 88-99% across groups). The most specific was one cirrhosis code and one hospital diagnostic code (range 89-98% across groups). Two physician visit codes or a single hospital diagnostic code, death code, or procedure code combined with a code for cirrhosis were sensitive and specific for hepatocellular carcinoma (sensitivity 94-96%, specificity 93-98%).

CONCLUSION

These sensitive and specific algorithms can be used to define patient cohorts or detect clinical outcomes using health administrative data. Our results will facilitate research into the adequacy of screening and treatment for patients with chronic viral hepatitis or other liver diseases.

摘要

背景

为了评估筛查和治疗策略,需要大规模的真实世界数据来了解与肝病相关的结局。我们旨在验证卫生行政数据在识别已知肝病患者的肝硬化、失代偿性肝硬化和肝细胞癌方面的作用。

方法

从多伦多肝脏疾病中心的病毒性肝炎患者(2006-2014 年)和金斯敦健康科学中心肝病诊所的所有肝病患者(2013 年)中提取主要患者数据。我们将临床信息与卫生行政数据相关联,并针对临床参考标准测试了一系列编码算法。

结果

共提取了 6714 例患者的原始图表数据。单一医师就诊代码诊断肝硬化的敏感性(98-99%),单一医院诊断代码诊断肝硬化的特异性(91-96%)。失代偿性肝硬化的最敏感算法是单一肝硬化代码加任何以下情况之一:医院诊断代码、死亡代码或失代偿程序代码(各分组的敏感性范围为 88-99%)。最特异的算法是单一肝硬化代码和单一医院诊断代码(各分组的特异性范围为 89-98%)。两个医师就诊代码或单一医院诊断代码、死亡代码或失代偿程序代码加肝硬化代码,对肝细胞癌具有敏感性和特异性(敏感性 94-96%,特异性 93-98%)。

结论

这些敏感和特异的算法可用于使用卫生行政数据定义患者队列或检测临床结局。我们的研究结果将有助于研究慢性病毒性肝炎或其他肝病患者的筛查和治疗是否充分。

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