Comparing the performance of charlson and elixhauser comorbidity indices to predict in-hospital mortality among a Chinese population

Miao Cai, Echu Liu, Ruihua Zhang, Xiaojun Lin, Steven E. Rigdon, Zhengmin Qian, Rhonda Belue, Jen Jen Chang

Research output: Contribution to journalArticle

Abstract

Objective: Earlier comorbidity measures have been developed or validated using the North American population. This study aims to compare five Charlson or Elixhauser comorbidity indices to predict in-hospital mortality using a large electronic medical record database from Shanxi, China. Methods: Using the primary diagnosis code and surgery procedure codes, we identified four hospitalized patient cohorts, hospitalized between 2013 and 2017, in Shanxi, China, as follows: congestive heart failure (CHF, n=41,577), chronic renal failure (CRF, n=40,419), diabetes (n=171,355), and percutaneous coronary intervention (PCI, n=39,097). We used logistic regression models and c-statistics to evaluate the in-hospital mortality predictive performance of two multiple comorbidity indicator variables developed by Charlson in 1987 and Elixhauser in 1998 and three single numeric scores by Quan in 2011, van Walraven in 2009, and Moore 2017. Results: Elixhauser comorbidity indicator variables had consistently higher c-statistics (0.824, 0.843, 0.904, 0.853) than all other four comorbidity measures, across all four disease cohorts. Moore’s comorbidity score outperformed the other two score systems in CHF, CRF, and diabetes cohorts (c-statistics: 0.776, 0.832, 0.869), while van Walraven’s score outperformed all others among PCI patients (c-statistics: 0.827). Conclusion: Elixhauser comorbidity indicator variables are recommended, when applied to large Chinese electronic medical record databases, while Moore’s score system is appropriate for relatively small databases.

Original languageEnglish (US)
Pages (from-to)307-316
Number of pages10
JournalClinical Epidemiology
Volume12
DOIs
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Epidemiology

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