Division of Rheumatology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; Milan Ilich Arthritis Research Centre, 5591 No. 3 Rd, Richmond, British Columbia, Canada V6X 2C7.
Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
J Clin Epidemiol. 2015 Jan;68(1):3-14. doi: 10.1016/j.jclinepi.2014.09.010. Epub 2014 Oct 31.
To conduct a systematic review of studies reporting on the development or validation of comorbidity indices using administrative health data and compare their ability to predict outcomes related to comorbidity (ie, construct validity).
We conducted a comprehensive literature search of MEDLINE and EMBASE, until September 2012. After title and abstract screen, relevant articles were selected for review by two independent investigators. Predictive validity and model fit were measured using c-statistic for dichotomous outcomes and R(2) for continuous outcomes.
Our review includes 76 articles. Two categories of comorbidity indices were identified: those identifying comorbidities based on diagnoses, using International Classification of Disease codes from hospitalization or outpatient data, and based on medications, using pharmacy data. The ability of indices studied to predict morbidity-related outcomes ranged from poor (C statistic ≤ 0.69) to excellent (C statistic >0.80) depending on the specific index, outcome measured, and study population. Diagnosis-based measures, particularly the Elixhauser Index and the Romano adaptation of the Charlson Index, resulted in higher ability to predict mortality outcomes. Medication-based indices, such as the Chronic Disease Score, demonstrated better performance for predicting health care utilization.
A number of valid comorbidity indices derived from administrative data are available. Selection of an appropriate index should take into account the type of data available, study population, and specific outcome of interest.
系统评价使用医疗保健管理数据开发或验证合并症指数的研究，并比较它们预测与合并症相关结局（即结构有效性）的能力。
我们对 MEDLINE 和 EMBASE 进行了全面的文献检索，截至 2012 年 9 月。在标题和摘要筛选之后，两名独立的研究者选择了相关文章进行评估。使用二项结局的 C 统计量和连续结局的 R²来衡量预测有效性和模型拟合。
我们的综述包括 76 篇文章。确定了两类合并症指数：基于诊断的指数，使用住院或门诊数据的国际疾病分类代码；基于药物的指数，使用药房数据。所研究的指数预测与发病率相关结局的能力因特定指数、测量结局和研究人群而异，从较差（C 统计量≤0.69）到较好（C 统计量>0.80）。基于诊断的指标，特别是 Elixhauser 指数和 Charlson 指数的 Romano 改编版，对预测死亡率结局的能力更高。基于药物的指数，如慢性疾病评分，在预测医疗保健利用方面表现出更好的性能。
有许多从医疗保健管理数据中得出的有效合并症指数。选择合适的指数应考虑到可用数据的类型、研究人群和特定的研究结局。
