Dr Foster Unit, Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, UK.
Med Care. 2012 Dec;50(12):1109-18. doi: 10.1097/MLR.0b013e31825f64d0.
Adjustment for comorbidities is common in performance benchmarking and risk prediction. Despite the exponential upsurge in the number of articles citing or comparing Charlson, Elixhauser, and their variants, no systematic review has been conducted on studies comparing comorbidity measures in use with administrative data. We present a systematic review of these multiple comparison studies and introduce a new meta-analytical approach to identify the best performing comorbidity measures/indices for short-term (inpatient + ≤ 30 d) and long-term (outpatient+>30 d) mortality.
Articles up to March 18, 2011 were searched based on our predefined terms. The bibliography of the chosen articles and the relevant reviews were also searched and reviewed. Multiple comparisons between comorbidity measures/indices were split into all possible pairs. We used the hypergeometric test and confidence intervals for proportions to identify the comparators with significantly superior/inferior performance for short-term and long-term mortality. In addition, useful information such as the influence of lookback periods was extracted and reported.
Out of 1312 retrieved articles, 54 articles were eligible. The Deyo variant of Charlson was the most commonly referred comparator followed by the Elixhauser measure. Compared with baseline variables such as age and sex, comorbidity adjustment methods seem to better predict long-term than short-term mortality and Elixhauser seems to be the best predictor for this outcome. For short-term mortality, however, recalibration giving empirical weights seems more important than the choice of comorbidity measure.
The performance of a given comorbidity measure depends on the patient group and outcome. In general, the Elixhauser index seems the best so far, particularly for mortality beyond 30 days, although several newer, more inclusive measures are promising.
在绩效基准测试和风险预测中，合并症的调整很常见。尽管引用或比较 Charlson、Elixhauser 及其变体的文章数量呈指数级增长，但尚未对使用行政数据的合并症测量方法进行系统评价。我们对这些多项比较研究进行了系统评价，并提出了一种新的荟萃分析方法，以确定用于短期（住院+≤30 天）和长期（门诊+>30 天）死亡率的最佳合并症测量/指标。
根据我们的预定义术语，搜索截至 2011 年 3 月 18 日的文章。还搜索并审查了选定文章的参考文献和相关评论。将合并症测量/指标的多项比较分为所有可能的对。我们使用超几何检验和比例置信区间来确定短期和长期死亡率具有明显优越/较差性能的比较器。此外，还提取并报告了有用的信息，例如回溯期的影响。
从 1312 篇检索到的文章中，有 54 篇符合条件。Charlson 的 Deyo 变体是最常被引用的比较器，其次是 Elixhauser 测量。与年龄和性别等基线变量相比，合并症调整方法似乎更能预测长期而不是短期死亡率，Elixhauser 似乎是该结果的最佳预测指标。然而，对于短期死亡率，重新校准给予经验权重似乎比选择合并症测量更重要。
给定合并症测量的性能取决于患者群体和结果。一般来说，Elixhauser 指数似乎是迄今为止最好的，特别是对于超过 30 天的死亡率，尽管有几个更新、更全面的指标很有前途。
