Why Summary Comorbidity Measures Such As the Charlson Comorbidity Index and Elixhauser Score Work.
Why Summary Comorbidity Measures Such As the Charlson Comorbidity Index and Elixhauser Score Work.
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DOI：
10.1097/mlr.0b013e318297429c
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发表时间：
2015-09
期刊：
影响因子：
3
通讯作者：
Egleston BL
中科院分区：
文献类型：
--
作者：
Austin SR;Wong YN;Uzzo RG;Beck JR;Egleston BL
共病适应是卫生服务研究和临床预后的重要组成部分。在统计模型中调整合并症时，研究人员可以单独或通过使用Charlson合并症指数或Elixhauser评分等汇总指标来纳入合并症。我们研究了在何种条件下，个人与汇总措施是最合适的。我们提供了一个分析证明的效用合并症的总结措施时，用于取代个别合并症。我们使用SEER-医疗保险数据实例比较了Charlson和Elixhauser评分与预后模型中个体合并症的使用。我们使用模拟检查了汇总共病指标调整混杂因素的能力。我们设计了一个数学证明，发现合并症的总结措施是适当的预后或生存分析的调整机制。一旦知道了共病评分，通常就不需要关于用于创建评分的共病变量的其他信息。我们的数据示例和模拟在很大程度上证实了这一发现。总结合并症指标，如Charlson合并症指数和Elixhauser评分，常用于临床预后和合并症调整。我们已经提供了一个理论上的理由，证明在许多情况下使用这样的分数。我们的模拟总体上证实了汇总共病指标在卫生服务研究中作为使用个体共病变量的替代品的实用性。一个警告是，汇总度量可能只与用于创建它的变量一样好。
Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate. We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations. We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding. Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.