Falls and Injury Prevention Group, Neuroscience Research Australia, University of New South Wales, Barker Street, Randwick, NSW 2031, Australia.
Falls and Injury Prevention Group, Neuroscience Research Australia, University of New South Wales, Barker Street, Randwick, NSW 2031, Australia.
J Clin Epidemiol. 2015 Jan;68(1):44-51. doi: 10.1016/j.jclinepi.2014.09.017. Epub 2014 Oct 28.
To evaluate the performance of the Charlson Comorbidity Index (CCI) in the prediction of mortality, 30-day readmission, and length of stay (LOS) in a hip fracture population using algorithms designed for use in International Classification of Diseases, 10th Revision (ICD-10)--coded administrative data sets.
Hospitalization and death data for 47,698 New South Wales residents aged 65 years and over, admitted for hip fracture, were linked. Comorbidities were ascertained using ICD-10 coding algorithms developed by Sundararajan (2004) and Quan (2005). Regression models were fitted, and area under the receiver operating curve (AUC) and Akaike information criterion were assessed.
Both algorithms had acceptable discrimination in predicting in-hospital (AUC, 0.72-0.76), 30-day (0.72-0.75), and 1-year mortality (0.69-0.75) but poor ability to predict 30-day readmission (0.54-0.57) or LOS (adjusted R(2), 0.007-0.045). The Quan algorithm provided better model fit than the Sundararajan algorithm. Models incorporating comorbidities as individual variables performed better than the Charlson weighted or updated Quan weighted score. Including a 1-year lookback period increased predictive ability for 1-year mortality only.
The CCI is a valid tool for predicting mortality but not resource utilization after hip fracture. We recommend the use of the Quan algorithm rather than Sundararajan algorithm and to model individual conditions rather than categorized weighted scores.
评估 Charlson 合并症指数(CCI)在使用专为国际疾病分类第 10 版(ICD-10)编码的算法预测髋部骨折人群的死亡率、30 天再入院率和住院时间(LOS)方面的性能。
将新南威尔士州 47698 名 65 岁及以上髋部骨折住院患者的住院和死亡数据进行了链接。使用 Sundararajan(2004 年)和 Quan(2005 年)开发的 ICD-10 编码算法确定合并症。拟合回归模型,并评估接收者操作特征曲线下的面积(AUC)和赤池信息量准则。
两种算法在预测住院期间(AUC,0.72-0.76)、30 天(0.72-0.75)和 1 年死亡率(0.69-0.75)方面具有可接受的区分能力,但预测 30 天再入院率(0.54-0.57)或 LOS(调整后的 R(2),0.007-0.045)的能力较差。Quan 算法提供的模型拟合优于 Sundararajan 算法。将合并症作为个体变量纳入模型的表现优于 Charlson 加权或更新的 Quan 加权评分。仅在 1 年回顾期内增加了对 1 年死亡率的预测能力。
CCI 是预测髋部骨折后死亡率的有效工具,但不是资源利用的有效工具。我们建议使用 Quan 算法而不是 Sundararajan 算法,并且对个体情况进行建模而不是对分类加权评分进行建模。