Evidence map›Paper›PMID 25769057›Full record

ArticleMedical care2015

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

Nicolas R Thompson, Youran Fan, Jarrod E Dalton, Lara Jehi, Benjamin P Rosenbaum, Sumeet Vadera, Sandra D Griffith

Abstract read
In one paragraph

Article in Medical care, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
119citing papers in PubMed, 1 pooled it
10.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

119 citing papers in PubMed, 1 synthesis or guideline pooled it, 238 citations in OpenAlex.

  1. How to assess multimorbidity: a systematic review.Frontiers in public health · 2025
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  11. Reporting Quality for Comorbidity Adjustment in Studies Using Korean Health Insurance Claim Data: A Scoping Review.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2026
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  19. Referral Networks, Racial Inequity, and Hospital Quality for Open Heart Surgery.Circulation. Cardiovascular quality and outcomes · 2025
    Article
  20. Observational

59 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Nicolas R Thompson*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.
Youran Fan
Jarrod E Dalton
Lara Jehi
Benjamin P Rosenbaum
Sumeet Vadera
Sandra D Griffith
Cleveland Clinic · USEpilepsy Foundation · AUUniversity of California, Irvine · US

Funding

Clinical and Translational Science Collaborative of ClevelandKL2TR000440 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI KONSTAN, MICHAEL W. · 2012 to 2016
$11.1M
NCATS NIH HHS KL2 TR000440
6 · The paper itself

Abstract

backgroundRecently, 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.

methodsUsing 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.

resultsWeights 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).

conclusionsThe 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.

Indexed as

ComorbidityHealth Status IndicatorsHospital MortalityHumansInpatientsMaryland

Identifiers

PMID25769057
PMCPMC4812819
OpenAlexW2038927461

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.