Gutacker Nils, Bloor Karen, Cookson Richard
1 Centre for Health Economics, University of York, England, UK
2 Department of Health Sciences, University of York, England, UK.
Eur J Public Health. 2015 Feb;25 Suppl 1:15-20. doi: 10.1093/eurpub/cku221.
The Charlson and Elixhauser comorbidity measures are commonly used methods to account for patient comorbidities in hospital-level comparisons of clinical quality using administrative data. Both have been validated in North America, but there is less evidence of their performance in Europe and in pooled cross-country data, which are features of the European Collaboration for Healthcare Optimization (ECHO) project. This study compares the performance of the Charlson/Deyo and Elixhauser comorbidity measures in predicting in-hospital mortality using data from five European countries in three inpatient groups.
Administrative data is used from five countries in 2008-2009 for three indicators commonly used in hospital quality comparisons: mortality rates following acute myocardial infarction, coronary artery bypass graft surgery and stroke. Logistic regression models are constructed to predict mortality controlling for age, gender and the relevant comorbidity measure. Model discrimination is evaluated using c-statistics. Model calibration is evaluated using calibration slopes. Overall goodness-of-fit is evaluated using Nagelkerke's R(2) and the Akaike information criterion. All models are validated internally by using bootstrapping and externally by using the 2009 model parameters to predict mortality in 2008.
The Elixhauser measure has better overall predictive ability in terms of discrimination and goodness-of-fit than the Charlson/Deyo measure or the age-sex only model. There is no clear difference in model calibration. These findings are robust to the choice of country, to pooling all five countries and to internal and external validation.
The Elixhauser list contains more comorbidities, which may enable it to achieve better discrimination than the Charlson measure. Both measures achieve similar calibration, so for the purpose of ECHO we judged the Elixhauser measure to be preferable.
在利用行政数据进行医院层面临床质量比较时，Charlson和Elixhauser共病度量法是常用的考量患者共病情况的方法。这两种方法在北美都已得到验证，但在欧洲以及汇总的跨国数据中，关于它们性能的证据较少，而这正是欧洲医疗优化协作组织（ECHO）项目的特点。本研究使用来自三个住院患者组的五个欧洲国家的数据，比较了Charlson/Deyo和Elixhauser共病度量法在预测住院死亡率方面的性能。
使用2008 - 2009年五个国家的行政数据，这些数据涉及医院质量比较中常用的三个指标：急性心肌梗死后死亡率、冠状动脉搭桥手术死亡率和中风死亡率。构建逻辑回归模型来预测死亡率，并对年龄、性别和相关共病度量进行控制。使用c统计量评估模型辨别力。使用校准斜率评估模型校准情况。使用Nagelkerke的R²和赤池信息准则评估整体拟合优度。所有模型通过自抽样进行内部验证，并通过使用2009年模型参数预测2008年死亡率进行外部验证。
就辨别力和拟合优度而言，Elixhauser度量法比Charlson/Deyo度量法或仅考虑年龄和性别的模型具有更好的整体预测能力。模型校准方面没有明显差异。这些发现对于国家的选择、将所有五个国家的数据汇总以及内部和外部验证而言都是稳健的。
Elixhauser列表包含更多共病情况，这可能使其比Charlson度量法具有更好的辨别力。两种度量法的校准情况相似，因此就ECHO项目而言，我们认为Elixhauser度量法更优。
