Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations
Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations
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DOI：
10.1097/00005650-200107000-00009
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发表时间：
2001-07-01
期刊：
影响因子：
3
通讯作者：
Connors, AF
中科院分区：
文献类型：
--
作者：
Stukenborg, GJ;Wagner, DP;Connors, AF
目标。本研究比较了两种共病风险调整方法（Deyo等人对Charlson指数的改编法和Elixhauser等人的方法）在五组加利福尼亚医院有共同住院原因的患者中的表现，并评估了以前住院患者的信息对模型表现的贡献。加州医院出院摘要数据为1994年至1997年的历年被用来创建一个纵向数据集的病人在五个疾病组。估计了11个logistic回归模型来预测每组患者的住院死亡风险，两种合并症风险调整方法均适用于仅从指数住院获得的患者信息，以及从指数和既往住院获得的信息。在每次比较中，采用Elixhauser等方法的模型所展示的统计性能水平（受试者工作特征曲线下的面积）均优于采用Deyo等自适应方法的模型。尽管大多数患者可以从以前的住院记录中获得信息，但这些额外的信息只会对使用合并症风险调整方法的模型的性能产生很小的改善。Elixhauser等人仅使用指数住院信息的方法比Deyo等人使用所有确定的住院信息的适应方法实现了更好的区分。两种合并症风险调整方法在将住院指数和既往入院信息分离为合并症的独立指标时均能达到最佳效果。
OBJECTIVES. This study compares the performance of two comorbidity risk adjustment methods (the Deyo et al adaptation of the Charlson index and the Elixhauser et al method) in five groups of California hospital patients with common reasons for hospitalization, and assesses the contribution to model performance made by information drawn from prior hospital admissions.METHODS. California hospital discharge abstract data for the calendar years 1994 through 1997 were used to create a longitudinal data set for patients in the five disease groups. Eleven logistic regression models were estimated to predict the risk of in-hospital death for patients in each group, with both comorbidity risk adjustment methods applied to patient information available from only the index hospitalization, and to information available from both the index and prior hospitalizations.RESULTS. For every comparison made, the level of statistical performance (area under the receiver operating characteristics curve) demonstrated by models using the Elixhauser et al method was superior to that of models using the Deyo et al adaptation method. Although most patients have information available from prior hospital admissions, this additional information yields only small improvements in the performance of models using either comorbidity risk adjustment method.CONCLUSIONS. Better discrimination is achieved with the Elixhauser et al method using only information from the index hospitalization than is achieved with the Deyo et al adaptation using information from all identified hospital admissions. Both comorbidity risk adjustment methods achieve their best performance when information from the index hospitalization and prior admissions is separated into independent indicators of comorbid illness.