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Reliability of comorbidity scores derived from administrative data in the tertiary hospital intensive care setting: a cross-sectional study

Overview of attention for article published in BMJ Health & Care Informatics, April 2019
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Title
Reliability of comorbidity scores derived from administrative data in the tertiary hospital intensive care setting: a cross-sectional study
Published in
BMJ Health & Care Informatics, April 2019
DOI 10.1136/bmjhci-2019-000016
Pubmed ID
Authors

Michael Hua-Gen Li, Anastasia Hutchinson, Mark Tacey, Graeme Duke

Abstract

Hospital reporting systems commonly use administrative data to calculate comorbidity scores in order to provide risk-adjustment to outcome indicators. We aimed to elucidate the level of agreement between administrative coding data and medical chart review for extraction of comorbidities included in the Charlson Comorbidity Index (CCI) and Elixhauser Index (EI) for patients admitted to the intensive care unit of a university-affiliated hospital. We conducted an examination of a random cross-section of 100 patient episodes over 12 months (July 2012 to June 2013) for the 19 CCI and 30 EI comorbidities reported in administrative data and the manual medical record system. CCI and EI comorbidities were collected in order to ascertain the difference in mean indices, detect any systematic bias, and ascertain inter-rater agreement. We found reasonable inter-rater agreement (kappa (κ) coefficient ≥0.4) for cardiorespiratory and oncological comorbidities, but little agreement (κ<0.4) for other comorbidities. Comorbidity indices derived from administrative data were significantly lower than from chart review: -0.81 (95% CI - 1.29 to - 0.33; p=0.001) for CCI, and -2.57 (95% CI -4.46 to -0.68; p=0.008) for EI. While cardiorespiratory and oncological comorbidities were reliably coded in administrative data, most other comorbidities were under-reported and an unreliable source for estimation of CCI or EI in intensive care patients. Further examination of a large multicentre population is required to confirm our findings.

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The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 20%
Student > Bachelor 3 15%
Student > Ph. D. Student 3 15%
Other 1 5%
Researcher 1 5%
Other 1 5%
Unknown 7 35%
Readers by discipline Count As %
Medicine and Dentistry 6 30%
Nursing and Health Professions 3 15%
Mathematics 1 5%
Psychology 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 0 0%
Unknown 8 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 November 2021.
All research outputs
#20,735,845
of 26,338,415 outputs
Outputs from BMJ Health & Care Informatics
#436
of 511 outputs
Outputs of similar age
#268,122
of 367,785 outputs
Outputs of similar age from BMJ Health & Care Informatics
#13
of 15 outputs
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