[Comparative study on three algorithms of the ICD-10 Charlson comorbidity index with myocardial infarction patients].
[Comparative study on three algorithms of the ICD-10 Charlson comorbidity index with myocardial infarction patients].
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DOI：
10.3961/jpmph.2010.43.1.42
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发表时间：
2010-01-01
期刊：
影响因子：
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通讯作者：
Kim, Kyoung Hoon
中科院分区：
文献类型：
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作者：
Kim, Kyoung Hoon
目的：比较三种国际疾病统计分类第10版翻译的Charlson合并症在预测住院心肌梗死（MI）患者中的表现。方法：确定2006年首次入院的≥ 20岁MI患者（n= 20，280）。Charlson合并症来自韩国健康保险审查和评估服务管理的健康保险索赔数据。各种条件的比较包括（a）三种算法（Halfon、Sundararajan和Quan算法），（B）回顾期（1年、3年和5年），（c）数据范围（入院数据、入院和门诊数据），和（d）诊断范围（主要诊断和第一次次要诊断、所有诊断）。每个程序的性能进行了测量与c-统计来自多元逻辑回归调整年龄，性别，入院类型和Charlson合并症指数。结果：在20,280例患者中，平均年龄为63.3岁，67.8%为男性，7.1%在住院期间死亡。Quan和Sundararajan算法比Halfon算法产生更高的患病率。Quan算法的c-统计量在所有条件下均略高于其他两种算法，但无显著差异。没有证据表明，在较长的回顾期，额外的数据，诊断提高了预测能力。结论：在医疗服务研究的MI患者使用健康保险索赔数据，目前的结果表明，全算法使用1年的回顾，涉及主要诊断和第一次辅助诊断是足够的预测住院死亡率。
OBJECTIVES: To compare the performance of three International Statistical Classification of Diseases, 10th Revision translations of the Charlson comorbidities when predicting in-hospital among patients with myocardial infarction (MI).METHODS: MI patients > or =20 years of age with the first admission during 2006 were identified(n=20,280). Charlson comorbidities were drawn from Heath Insurance Claims Data managed by Health Insurance Review and Assessment Service in Korea. Comparisions for various conditions included (a) three algorithms (Halfon, Sundararajan, and Quan algorithms), (b) lookback periods (1-, 3- and 5-years), (c) data range (admission data, admission and ambulatory data), and (d) diagnosis range (primary diagnosis and first secondary diagnoses, all diagnoses). The performance of each procedure was measured with the c-statistic derived from multiple logistic regression adjusted for age, sex, admission type and Charlson comorbidity index. A bootstrapping procedure was done to determine the approximate 95% confidence interval.RESULTS: Among the 20,280 patients, the mean age was 63.3 years, 67.8% were men and 7.1% died while hospitalized. The Quan and Sundararajan algorithms produced higher prevalences than the Halfon algorithm. The c-statistic of the Quan algorithm was slightly higher, but not significantly different, than that of other two algorithms under all conditions. There was no evidence that on longer lookback periods, additional data, and diagnoses improved the predictive ability.CONCLUSIONS: In health services study of MI patients using Health Insurance Claims Data, the present results suggest that the Quan Algorithm using a 1-year lookback involving primary diagnosis and the first secondary diagnosis is adequate in predicting in-hospital mortality.