Southern Danielle A, Quan Hude, Ghali William A
Department of Community Health Sciences and the Centre for Health and Policy Studies, University of Calgary, Calgary, Alberta, Canada.
Med Care. 2004 Apr;42(4):355-60. doi: 10.1097/01.mlr.0000118861.56848.ee.
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.
We compared the Charlson/Deyo and Elixhauser methods using Canadian administrative data on patients with myocardial infarction (MI).
We conducted a historical cohort study.
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.
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.
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%).
The Elixhauser comorbidity measurement method performs better than the widely used Charlson/Deyo method in the Canadian acute MI cases studied.
共病风险调整方法已广泛应用于行政数据，而Charlson/Deyo方法可能是文献中最常用的。然而，Elixhauser等人定义的一种新方法最近已被引入，可能更具优势，尽管尚未得到广泛验证。
我们使用加拿大心肌梗死（MI）患者的行政数据比较了Charlson/Deyo方法和Elixhauser方法。
我们进行了一项历史性队列研究。
我们使用了加拿大一个大城市1995年1月1日至2001年3月31日期间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主要诊断。
我们使用这两种方法中的每一种，根据每个病例记录中存在的国际疾病分类第九版临床修订版代码来定义共病变量。然后，我们比较了基于每种方法定义的变量的存在与否来预测住院死亡率的两个模型。生成了频率表，并计算了c统计量和-2对数似然值（-2LogL）的变化。我们还通过绘制两个模型定义的风险类别增加时观察到的和预期的死亡百分比，直观地评估了模型性能。
Elixhauser模型在预测死亡率方面优于Charlson/Deyo模型，c统计量值更高（0.793对0.704）。当绘制不同风险分组的预期和观察到的死亡风险时，Elixhauser方法的优越性能得到了证实，其中Elixhauser方法在不同分组中产生的预测和观察到的死亡概率范围（2.5%-33%）比Charlson/Deyo方法（5%-25%）更广。
在研究的加拿大急性心肌梗死病例中，Elixhauser共病测量方法比广泛使用的Charlson/Deyo方法表现更好。
