Institute of Health and Welfare Policy, School of Medicine, National Yang-Ming University, Taipei, Taiwan.
BMC Health Serv Res. 2010 May 27;10:140. doi: 10.1186/1472-6963-10-140.
It is important to find a comorbidity measure with better performance for use with administrative data. The new method proposed by Elixhauser et al. has never been validated and compared to the widely used Charlson method in the Asia region. The objective of this study was to compare the performance of three comorbidity measures using information from different data periods in predicting short- and long-term mortality among patients with acute myocardial infarction (AMI) and chronic obstructive pulmonary disease (COPD).
We conducted a retrospective cohort study using National Health Insurance claims data (2001-2002) in Taiwan. We constructed the Elixhauser, the Charlson/Deyo, and the Charlson/Romano methods based on the International Classification of Disease, 9th Revision, Clinical Modification codes in the claims data. Two data periods, including the index hospitalization as well as the index and prior 1-year hospitalizations, were used in the analysis. The performances were compared using the c-statistics derived from multiple logistic regression models that included age, gender, race, and whether the patient received surgery or not. The outcomes of interest were in-hospital and 1-year mortality.
The performance was in the same rank order among both populations regardless of the outcome and data period: Elixhauser > Charlson/Romano > Charlson/Deyo. In predicting in-hospital mortality, the Elixhauser models using information from the index hospitalization performed best, even better than the Charlson/Deyo or Charlson/Romano models using information from the index and prior hospitalizations. Nevertheless, in predicting 1-year mortality, the Elixhauser models using information from the index and 1-year prior hospitalizations performed better than using information from the index hospitalization only.
This is so far the first study to validate the Elixhauser method and compare it to other methods in the Asia region, and is the first to report its differences in data periods between short- and long-term outcomes. The comorbidity measurement developed by Elixhauser et al. has relatively good predictive validity, and researchers should consider its use in claims-based studies.
找到一种具有更好性能的合并症测量方法用于管理数据非常重要。Elixhauser 等人提出的新方法从未经过验证,也没有与亚洲地区广泛使用的 Charlson 方法进行比较。本研究的目的是比较三种合并症测量方法在预测急性心肌梗死(AMI)和慢性阻塞性肺疾病(COPD)患者短期和长期死亡率方面的性能,使用来自不同数据时期的信息。
我们使用台湾全民健康保险理赔数据(2001-2002 年)进行回顾性队列研究。我们根据理赔数据中的国际疾病分类,第 9 版,临床修正代码构建了 Elixhauser、Charlson/Deyo 和 Charlson/Romano 方法。分析中使用了两个数据时期,包括索引住院以及索引和前 1 年的住院。使用包括年龄、性别、种族以及患者是否接受手术在内的多因素逻辑回归模型的 c 统计量来比较性能。感兴趣的结果是住院内和 1 年死亡率。
无论结果和数据时期如何,在两种人群中表现均呈相同等级顺序:Elixhauser > Charlson/Romano > Charlson/Deyo。在预测住院内死亡率方面,使用索引住院信息的 Elixhauser 模型表现最佳,甚至优于使用索引和前住院信息的 Charlson/Deyo 或 Charlson/Romano 模型。然而,在预测 1 年死亡率方面,使用索引和 1 年前住院信息的 Elixhauser 模型比仅使用索引住院信息的模型表现更好。
这是迄今为止第一项在亚洲地区验证 Elixhauser 方法并将其与其他方法进行比较的研究,也是第一项报告其在短期和长期结果之间的数据时期差异的研究。Elixhauser 等人开发的合并症测量方法具有相对较好的预测有效性,研究人员应考虑在基于理赔的研究中使用该方法。