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Assessing measures of comorbidity and functional status for risk adjustment to compare hospital performance for colorectal cancer surgery: a retrospective data-linkage study

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2015
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Title
Assessing measures of comorbidity and functional status for risk adjustment to compare hospital performance for colorectal cancer surgery: a retrospective data-linkage study
Published in
BMC Medical Informatics and Decision Making, July 2015
DOI 10.1186/s12911-015-0175-1
Pubmed ID
Authors

Timothy A Dobbins, Tim Badgery-Parker, David C Currow, Jane M Young

Abstract

Comparing outcomes between hospitals requires consideration of patient factors that could account for any observed differences. Adjusting for comorbid conditions is common when studying outcomes following cancer surgery, and a commonly used measure is the Charlson comorbidity index. Other measures of patient health include the ECOG performance status and the ASA physical status score. This study aimed to ascertain how frequently ECOG and ASA scores are recorded in population-based administrative data collections in New South Wales, Australia and to assess the contribution each makes in addition to the Charlson comorbidity index in risk adjustment models for comparative assessment of colorectal cancer surgery outcomes between hospitals. We used linked administrative data to identify 6964 patients receiving surgery for colorectal cancer in 2007 and 2008. We summarised the frequency of missing data for Charlson comorbidity index, ECOG and ASA scores, and compared patient characteristics between those with and without these measures. The performance of ASA and ECOG in risk adjustment models that also included Charlson index was assessed for three binary outcomes: 12-month mortality, extended length of stay and 28-day readmission. Patient outcomes were compared between hospital peer groups using multilevel logistic regression analysis. The Charlson comorbidity index could be derived for all patients, ASA score was recorded for 78 % of patients and ECOG performance status recorded for only 24 % of eligible patients. Including ASA or ECOG improved the predictive ability of models, but there was no consistently best combination. The addition of ASA or ECOG did not substantially change parameter estimates for hospital peer group after adjusting for Charlson comorbidity index. While predictive ability of regression models is maximised by inclusion of one or both of ASA score and ECOG performance status, there is little to be gained by adding ASA or ECOG to models containing the Charlson comorbidity index to address confounding. The Charlson comorbidity index has good performance and is an appropriate measure to use in risk adjustment to compare outcomes between hospitals.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 12%
Researcher 8 12%
Student > Master 8 12%
Student > Bachelor 8 12%
Other 4 6%
Other 15 22%
Unknown 18 26%
Readers by discipline Count As %
Medicine and Dentistry 26 38%
Business, Management and Accounting 3 4%
Agricultural and Biological Sciences 2 3%
Mathematics 2 3%
Social Sciences 2 3%
Other 9 13%
Unknown 25 36%
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 17 July 2015.
All research outputs
#18,418,919
of 22,817,213 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,571
of 1,988 outputs
Outputs of similar age
#189,024
of 262,607 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#30
of 37 outputs
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