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Med Care. 2015 Apr;53(4):374-9. doi: 10.1097/MLR.0000000000000326.

A new Elixhauser-based comorbidity summary measure to predict in-hospital mortality.

Author information

1
*Department of Quantitative Health Sciences †Neurological Institute Center for Outcomes Research and Evaluation ‡Department of Outcomes Research §Neurological Institute ∥Epilepsy Center ¶Department of Neurosurgery, Cleveland Clinic, Cleveland, OH #Department of Neurosurgery, University of California, Irvine, Orange, CA.

Abstract

BACKGROUND:

Recently, van Walraven developed a weighted summary score (VW) based on the 30 comorbidities from the Elixhauser comorbidity system. One of the 30 comorbidities, cardiac arrhythmia, is currently excluded as a comorbidity indicator in administrative datasets such as the Nationwide Inpatient Sample (NIS), prompting us to examine the validity of the VW score and its use in the NIS.

METHODS:

Using data from the 2009 Maryland State Inpatient Database, we derived weighted summary scores to predict in-hospital mortality based on the full (30) and reduced (29) set of comorbidities and compared model performance of these and other comorbidity summaries in 2009 NIS data.

RESULTS:

Weights of our derived scores were not sensitive to the exclusion of cardiac arrhythmia. When applied to NIS data, models containing derived summary scores performed nearly identically (c statistics for 30 and 29 variable-derived summary scores: 0.804 and 0.802, respectively) to the model using all 29 comorbidity indicators (c=0.809), and slightly better than the VW score (c=0.793). Each of these models performed substantially better than those based on a simple count of Elixhauser comorbidities (c=0.745) or a categorized count (0, 1, 2, or ≥ 3 comorbidities; c=0.737).

CONCLUSIONS:

The VW score and our derived scores are valid in the NIS and are statistically superior to summaries using simple comorbidity counts. Researchers wishing to summarize the Elixhauser comorbidities with a single value should use the VW score or those derived in this study.

PMID:
25769057
PMCID:
PMC4812819
DOI:
10.1097/MLR.0000000000000326
[Indexed for MEDLINE]
Free PMC Article

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