Kumar Amit, Karmarkar Amol, Downer Brian, Vashist Amit, Adhikari Deepak, Al Snih Soham, Ottenbacher Kenneth
Center for Gerontology and Health Care Research, School of Public Health, Brown University, Providence, Rhode Island.
University of Texas Medical Branch, Galveston.
Arthritis Care Res (Hoboken). 2017 Nov;69(11):1668-1675. doi: 10.1002/acr.23195. Epub 2017 Oct 9.
To compare the performances of 3 comorbidity indices, the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, and the Centers for Medicare & Medicaid Services (CMS) risk adjustment model, Hierarchical Condition Category (HCC), in predicting post-acute discharge settings and hospital readmission for patients after joint replacement.
A retrospective study of Medicare beneficiaries with total knee replacement (TKR) or total hip replacement (THR) discharged from hospitals in 2009-2011 (n = 607,349) was performed. Study outcomes were post-acute discharge setting and unplanned 30-, 60-, and 90-day hospital readmissions. Logistic regression models were built to compare the performance of the 3 comorbidity indices using C statistics. The base model included patient demographics and hospital use. Subsequent models included 1 of the 3 comorbidity indices. Additional multivariable logistic regression models were built to identify individual comorbid conditions associated with high risk of hospital readmissions.
The 30-, 60-, and 90-day unplanned hospital readmission rates were 5.3%, 7.2%, and 8.5%, respectively. Patients were most frequently discharged to home health (46.3%), followed by skilled nursing facility (40.9%) and inpatient rehabilitation facility (12.7%). The C statistics for the base model in predicting post-acute discharge setting and 30-, 60-, and 90-day readmission in TKR and THR were between 0.63 and 0.67. Adding the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, or HCC increased the C statistic minimally from the base model for predicting both discharge settings and hospital readmission. The health conditions most frequently associated with hospital readmission were diabetes mellitus, pulmonary disease, arrhythmias, and heart disease.
The comorbidity indices and CMS-HCC demonstrated weak discriminatory ability to predict post-acute discharge settings and hospital readmission following joint replacement.
比较3种合并症指数,即查尔森合并症指数、埃利克斯豪泽合并症指数以及医疗保险和医疗补助服务中心(CMS)风险调整模型——分层疾病分类(HCC),在预测关节置换术后患者急性后期出院安置情况和医院再入院方面的表现。
对2009 - 2011年从医院出院的接受全膝关节置换术(TKR)或全髋关节置换术(THR)的医疗保险受益人进行回顾性研究(n = 607,349)。研究结局为急性后期出院安置情况以及非计划的30天、60天和90天医院再入院情况。构建逻辑回归模型,使用C统计量比较3种合并症指数的表现。基础模型包括患者人口统计学特征和医院使用情况。后续模型纳入3种合并症指数中的1种。构建额外的多变量逻辑回归模型,以识别与医院再入院高风险相关的个体合并症情况。
3个非计划的30天、60天和90天医院再入院率分别为5.3%、7.2%和8.5%。患者最常出院至家庭健康护理机构(46.3%),其次是专业护理机构(40.9%)和住院康复机构(12.7%)。基础模型在预测TKR和THR术后急性后期出院安置情况以及30天、60天和90天再入院方面的C统计量在0.63至0.67之间。添加查尔森合并症指数、埃利克斯豪泽合并症指数或HCC后,在预测出院安置情况和医院再入院方面,相较于基础模型,C统计量的提升微乎其微。与医院再入院最常相关的健康状况为糖尿病、肺部疾病、心律失常和心脏病。
合并症指数和CMS - HCC在预测关节置换术后急性后期出院安置情况和医院再入院方面的鉴别能力较弱。