Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data The AHRQ Elixhauser Comorbidity Index
Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data The AHRQ Elixhauser Comorbidity Index
复制标题
DOI：
10.1097/mlr.0000000000000735
复制
发表时间：
2017-07-01
期刊：
影响因子：
3
通讯作者：
Elixhauser, Anne
中科院分区：
文献类型：
--
作者：
Moore, Brian J.;White, Susan;Elixhauser, Anne
目的：我们通过开发2个指数扩展了关于合并症测量的文献，基于Elixhauser合并症测量，旨在预测2个经常报告的健康结局：住院死亡率和30天再入院的管理数据。Elixhauser测量通常用于研究中，作为控制疾病严重程度的调整因子。数据来源：我们使用了一个大型分析文件，该文件是从2011年和2012年18个州的医疗保健成本和利用项目州住院患者数据库中的所有付款人医院管理数据构建的。最终的模型是通过对每个结果进行反向逐步logistic回归的自举复制得出的。为每个Elixhauser合并症生成比值比和指数权重，以创建每个死亡率和再入院记录的单一指数评分。模型验证进行了c-statistics.Results：我们的指数得分进行，以及分别使用所有29 Elixhauser共变数。在不包括其他协变量的情况下，我们的指数评分的c-统计量对于死亡指数为0.777（95%置信区间，0.776-0.778），对于再入院指数为0.634（95%置信区间，0.633-0.634）。这些指数在由人口统计学特征或临床状况定义的多个子样本中保持稳定。其他常用的协变量（年龄，性别，预期付款人）的加入，改善歧视moderately.Conclusions：这些指数是有效的方法，将共病条件的影响模型设计，以评估在医院的死亡率和再入院的风险，使用有限的临床信息的行政数据，特别是当小样本量是一个问题。
Objective: We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly used in research as an adjustment factor to control for severity of illness.Data Sources: We used a large analysis file built from all-payer hospital administrative data in the Healthcare Cost and Utilization Project State Inpatient Databases from 18 states in 2011 and 2012.Methods: The final models were derived with bootstrapped replications of backward stepwise logistic regressions on each outcome. Odds ratios and index weights were generated for each Elixhauser comorbidity to create a single index score per record for mortality and readmissions. Model validation was conducted with c-statistics.Results: Our index scores performed as well as using all 29 Elixhauser comorbidity variables separately. The c-statistic for our index scores without inclusion of other covariates was 0.777 (95% confidence interval, 0.776-0.778) for the mortality index and 0.634 (95% confidence interval, 0.633-0.634) for the readmissions index. The indices were stable across multiple subsamples defined by demographic characteristics or clinical condition. The addition of other commonly used covariates (age, sex, expected payer) improved discrimination modestly.Conclusions: These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.