Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data
Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data
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DOI：
10.1097/01.mlr.0000118861.56848.ee
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发表时间：
2004-04-01
期刊：
影响因子：
3
通讯作者：
Ghali, WA
中科院分区：
文献类型：
--
作者：
Southern, DA;Quan, H;Ghali, WA
背景：共病风险调整方法已被广泛应用于管理数据中，Charlson/Deyo方法可能是文献中最常用的方法。然而，Elixhauser等人最近提出了一种新方法，尽管尚未得到广泛验证，但它可能更优越。目的：我们比较Charlson/Deyo方法和Elixhauser方法对心肌梗死（MI）患者的治疗数据。研究设计：我们进行了一项历史队列研究。研究对象：我们使用了1995年1月1日至2001年3月31日期间加拿大一个大城市的所有急性心肌梗死病例的行政医院出院数据，这些病例被编码为最负责任的诊断。方法：我们根据每个病例记录中存在的《国际疾病分类》第九版临床修改代码，使用两种方法中的每一种来定义合并症变量。然后，我们比较了基于每种方法定义的变量是否存在的两种预测住院死亡率的模型。生成频率表，计算c统计量和-2对数似然（-2LogL）的变化。我们还通过绘制由两个模型定义的增加风险类别的观察和预期死亡百分比来直观地评估模型的性能。结果：Elixhauser模型预测死亡率优于Charlson/Deyo模型，c统计值更高（0.793比0.704）。Elixhauser方法的优越性能在绘制跨风险增加分组的预期和观察死亡风险时得到证实，其中Elixhauser方法产生的跨分组的预测和观察死亡概率范围（2.5%-33%）比Charlson/Deyo方法（5%-25%）更大。结论：在所研究的加拿大急性心肌梗死病例中，Elixhauser共病测量方法优于广泛使用的Charlson/Deyo方法。
Background: Comorbidity risk adjustment methods have been used widely with administrative data, and the Charlson/Deyo method is perhaps the most commonly used in the literature. However, a new method defined by Elixhauser et al. has been introduced recently and could be superior, although it has not been validated widely.Objectives: We compared the Charlson/Deyo and Elixhauser methods using Canadian administrative data on patients with myocardial infarction (MI).Research Design: We conducted a historical cohort study.Subjects: We used administrative hospital discharge data from a large Canadian city for all cases with acute MI coded as most responsible diagnosis between January 1, 1995, and March 31, 2001.Measures: We used each of the 2 methods to define comorbidity variables based on the International Classification of Diseases, 9th Revision, Clinical Modification codes present in each case record. We then compared 2 models predicting in-hospital mortality based on presence or absence of the variables defined by each of the methods. Frequency tables were produced and c-statistics and changes in -2 log likelihood (-2LogL) were calculated. We also visually assessed model performance by plotting observed and expected percentages of death for increasing risk categories defined by the 2 models.Results: The Elixhauser model outperformed the Charlson/Deyo model in predicting mortality, with higher c-statistic values (0.793 vs. 0.704). Superior performance of the Elixhauser method is confirmed when plotting the expected and observed risks of death across groupings of increasing risk, in which the Elixhauser method yields a wider range of predicted and observed probabilities of death across groupings (2.5%-33%) than does the Charlson/Deyo method (5%-25%).Conclusions: The Elixhauser comorbidity measurement method performs better than the widely used Charlson/Deyo method in the Canadian acute MI cases studied.