Cross-national comparative performance of three versions of the ICD-10 Charlson index
Cross-national comparative performance of three versions of the ICD-10 Charlson index
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DOI：
10.1097/mlr.0b013e3181484347
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发表时间：
2007-12-01
期刊：
影响因子：
3
通讯作者：
Ghali, William A.
中科院分区：
文献类型：
--
作者：
Sundararajan, Vijaya;Quan, Hude;Ghali, William A.
目的：Charlson共病指数已被广泛用于使用行政健康数据进行结果研究的风险调整。最近，《国际疾病统计分类第十版》(ICD-10)关于查尔逊合并症的3个译本已经出版。方法：对澳大利亚(N=2000-2001，最多25个诊断代码)、加拿大(N=2002-2003，最多16个诊断代码)、瑞士(N=1999-2001，不限诊断代码)和日本(N=2003，最多11个诊断代码)的数据进行分析。只包括年龄在18岁及以上、住院时间为2天的首次入院患者。对于每个算法，以住院死亡率为结果，以Charlson个体合并症或Charlson指数评分为自变量，建立了2个Logistic回归模型。使用c统计量(代表受试者工作特征曲线下的面积)及其95%的概率自举分布来评估模型的性能。结果：总体上，在每个人群的数据中，3种翻译的共病水平类别的分布是相似的。在所有数据集中，全版本比Halfon或Sundararajan版本产生的中位数c-统计量略高。例如，在日本的数据中，使用个人共病系数，c统计量的中位数分别为0.712(全)、0.709(孙达拉真)和0.694(半峰)。结论：我们的分析表明，所有ICD-10版本的Charlson算法都表现出令人满意的表现(c-统计量0.70-0.86)，其中全版本在所有数据集中表现出优于其他版本的趋势。
Objective: The Charlson comorbidity index has been widely used for risk adjustment in outcome studies using administrative health data. Recently, 3 International Statistical Classification of Diseases, Tenth Revision (ICD-10) translations have been published for the Charlson comorbidities. This study was conducted to compare the predictive performance of these versions (the Halfon, Sundararajan, and Quan versions) of the ICD-10 coding algorithms using data from 4 countries.Methods: Data from Australia (N = 2000-2001, max 25 diagnosis codes), Canada (N = 2002-2003, max 16 diagnosis codes), Switzerland (N = 1999-2001, unlimited number of diagnosis codes), and Japan (N = 2003, max 11 diagnosis codes) were analyzed. Only the first admission for patients age 18 years and older, with a length of stay of >= 2 days was included. For each algorithm, 2 logistic regression models were fitted with hospital mortality as the outcome and the Charlson individual comorbidities or the Charlson index score as independent variables. The c-statistic (representing the area under the receiver operating characteristic curve) and its 95% probability bootstrap distribution were employed to evaluate model performance.Results: Overall, within each population's data, the distribution of comorbidity level categories was similar across the 3 translations. The Quan version produced slightly higher median c-statistics than the Halfon or Sundararajan versions in all datasets. For example, in Japanese data, the median c-statistics were 0.712 (Quan), 0.709 (Sundararajan), and 0.694 (Halfon) using individual comorbidity coefficients. In general, the probability distributions between the Quan and the Sundararajan versions overlapped, whereas those between the Quan and the Halfon version did not.Conclusions: Our analyses show that all of the ICD-10 versions of the Charlson algorithm performed satisfactorily (c-statistics 0.70-0.86), with the Quan version showing a trend toward outperforming the other versions in all data sets.