Schneeweiss S, Maclure M
Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
Int J Epidemiol. 2000 Oct;29(5):891-8. doi: 10.1093/ije/29.5.891.
Comorbidity scores are increasingly used to reduce potential confounding in epidemiological research. Our objective was to compare metrical and practical properties of published comorbidity scores for use in epidemiological research with administrative databases.
The literature was searched for studies of the validity of comorbidity scores as predictors of mortality and health service use, as measured by change in the area under the receiver operating characteristic (ROC) curve for dichotomous outcomes, and change in R(2) for continuous outcomes.
Six scores were identified, including four versions of the Charlson Index (CI) which use either the three-digit International Classification of Diseases, Ninth Revision (ICD-9) or the full ICD-9-CM (clinical modification) code, and two versions of the Chronic Disease Score (CDS) which used outpatient pharmacy records. Depending on the population and exposure under study, predictive validities varied between c = 0.64 and c = 0.77 for in-hospital or 30-day mortality. This is only a slight improvement over age adjustment. In one study the simple measure 'number of diagnoses' outperformed the CI (c = 0.73 versus c = 0.65). Proprietary scores like Ambulatory Diagnosis Groups and Patient Management Categories do not necessarily perform better in predicting mortality. Comorbidity indices are susceptible to a variety of coding errors.
Comorbidity scores, particularly the CDS or D'Hoore's CI based on three-digit ICD-9 codes, may be useful in exploratory data analysis. However, residual confounding by comorbidity is inevitable, given how these scores are derived. How much residual confounding usually remains is something that future studies of comorbidity scores should examine. In any given study, better control for confounding can be achieved by deriving study-specific weights, to aggregate comorbidities into groups with similar relative risks of the outcomes of interest.
合并症评分越来越多地用于减少流行病学研究中的潜在混杂因素。我们的目的是比较已发表的合并症评分在利用行政数据库进行的流行病学研究中的度量和实际属性。
检索文献，查找关于合并症评分作为死亡率和医疗服务利用预测指标有效性的研究，通过二分结局的受试者工作特征（ROC）曲线下面积变化以及连续结局的R²变化来衡量。
识别出六个评分，包括四个版本的查尔森指数（CI），其使用三位数字的国际疾病分类第九版（ICD - 9）或完整的ICD - 9 - CM（临床修订版）编码，以及两个版本的慢性病评分（CDS），其使用门诊药房记录。根据所研究的人群和暴露情况，对于住院或30天死亡率，预测效度在c = 0.64至c = 0.77之间变化。这仅比年龄调整略有改善。在一项研究中，简单的“诊断数量”指标比CI表现更好（c = 0.73对c = 0.65）。诸如门诊诊断组和患者管理类别等专有评分在预测死亡率方面不一定表现更好。合并症指数容易出现各种编码错误。
合并症评分，特别是基于三位数字ICD - 9编码的CDS或德胡尔CI，可能在探索性数据分析中有用。然而，鉴于这些评分的推导方式，合并症导致的残余混杂是不可避免的。合并症评分的未来研究应考察通常会残留多少混杂因素。在任何特定研究中，通过推导特定研究权重，将合并症汇总为具有相似感兴趣结局相对风险的组，可以更好地控制混杂因素。
