Quebec National Institute of Public Health.
Department of Social and Preventive Medicine, Faculty of Medicine, Laval University.
Med Care. 2018 May;56(5):441-447. doi: 10.1097/MLR.0000000000000905.
To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems.
Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values.
Combined index [c-statistics: 0.853 (95% confidence interval: CI, 0.848-0.856)] performed better than original Charlson [0.841 (95% CI, 0.835-0.844)] or Elixhauser [0.841 (95% CI, 0.837-0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven: 0.852, Schneeweiss: 0.851, Charlson: 0.849). Results were consistent across both coding systems.
The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.
验证并比较将合并 Charlson 和 Elixhauser 指标的合并共病指数的国际疾病分类第 10 版 (ICD-10) 版本与个别指标在预测 30 天死亡率方面的性能。选择一种提供在 ICD-9 和 ICD-10 编码系统中性能最优的权重推导方法。
使用 2 个基于人群的住院患者队列,在 ICD-9(2005 年,n=337367)和 ICD-10(2011 年,n=348820)中,我们通过逻辑回归预测 30 天死亡率来验证合并共病指数。为了了解合并指数和两个单独指标的性能,考虑了影响指数性能的因素,如人口特征和权重推导方法。我们应用了 3 种评分方法(Van Walraven、Schneeweiss 和 Charlson),并确定哪种方法提供最佳预测值。
合并指数[C 统计量:0.853(95%置信区间:0.848-0.856)]在 ICD-10 队列中的表现优于原始 Charlson [0.841(95%CI,0.835-0.844)]或 Elixhauser [0.841(95%CI,0.837-0.844)]指标。所有权重推导方法为合并指数提供了接近高区分度的结果(Van Walraven:0.852,Schneeweiss:0.851,Charlson:0.849)。结果在两个编码系统中均一致。
合并指数在 ICD-9 和 ICD-10 编码系统中仍然有效,评估的 3 种权重推导方法在这些编码系统中提供了一致的高性能。