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Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases

BACKGROUND: Comorbidity measures, such as the Charlson Comorbidity Index (CCI) and Elixhauser Method (EM), are frequently used for risk-adjustment by healthcare researchers. This study sought to create CCI and EM lists of Read codes, which are standard terminology used in some large primary care dat...

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Autores principales: Metcalfe, David, Masters, James, Delmestri, Antonella, Judge, Andrew, Perry, Daniel, Zogg, Cheryl, Gabbe, Belinda, Costa, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6554904/
https://www.ncbi.nlm.nih.gov/pubmed/31170931
http://dx.doi.org/10.1186/s12874-019-0753-5
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author Metcalfe, David
Masters, James
Delmestri, Antonella
Judge, Andrew
Perry, Daniel
Zogg, Cheryl
Gabbe, Belinda
Costa, Matthew
author_facet Metcalfe, David
Masters, James
Delmestri, Antonella
Judge, Andrew
Perry, Daniel
Zogg, Cheryl
Gabbe, Belinda
Costa, Matthew
author_sort Metcalfe, David
collection PubMed
description BACKGROUND: Comorbidity measures, such as the Charlson Comorbidity Index (CCI) and Elixhauser Method (EM), are frequently used for risk-adjustment by healthcare researchers. This study sought to create CCI and EM lists of Read codes, which are standard terminology used in some large primary care databases. It also aimed to describe and compare the predictive properties of the CCI and EM amongst patients with hip fracture (and matched controls) in a large primary care administrative dataset. METHODS: Two researchers independently screened 111,929 individual Read codes to populate the 17 CCI and 31 EM comorbidity categories. Patients with hip fractures were identified (together with age- and sex-matched controls) from UK primary care practices participating in the Clinical Practice Research Datalink (CPRD). The predictive properties of both comorbidity measures were explored in hip fracture and control populations using logistic regression models fitted with 30- and 365-day mortality as the dependent variables together with tests of equality for Receiver Operating Characteristic (ROC) curves. RESULTS: There were 5832 CCI and 7156 EM comorbidity codes. The EM improved the ability of a logistic regression model (using age and sex as covariables) to predict 30-day mortality (AUROC 0.744 versus 0.686). The EM alone also outperformed the CCI (0.696 versus 0.601). Capturing comorbidities over a prolonged period only modestly improved the predictive value of either index: EM 1-year look-back 0.645 versus 5-year 0.676 versus complete record 0.695 and CCI 0.574 versus 0.591 versus 0.605. CONCLUSIONS: The comorbidity code lists may be used by future researchers to calculate CCI and EM using records from Read coded databases. The EM is preferable to the CCI but only marginal gains should be expected from incorporating comorbidities over a period longer than 1 year. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0753-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-65549042019-06-10 Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases Metcalfe, David Masters, James Delmestri, Antonella Judge, Andrew Perry, Daniel Zogg, Cheryl Gabbe, Belinda Costa, Matthew BMC Med Res Methodol Research Article BACKGROUND: Comorbidity measures, such as the Charlson Comorbidity Index (CCI) and Elixhauser Method (EM), are frequently used for risk-adjustment by healthcare researchers. This study sought to create CCI and EM lists of Read codes, which are standard terminology used in some large primary care databases. It also aimed to describe and compare the predictive properties of the CCI and EM amongst patients with hip fracture (and matched controls) in a large primary care administrative dataset. METHODS: Two researchers independently screened 111,929 individual Read codes to populate the 17 CCI and 31 EM comorbidity categories. Patients with hip fractures were identified (together with age- and sex-matched controls) from UK primary care practices participating in the Clinical Practice Research Datalink (CPRD). The predictive properties of both comorbidity measures were explored in hip fracture and control populations using logistic regression models fitted with 30- and 365-day mortality as the dependent variables together with tests of equality for Receiver Operating Characteristic (ROC) curves. RESULTS: There were 5832 CCI and 7156 EM comorbidity codes. The EM improved the ability of a logistic regression model (using age and sex as covariables) to predict 30-day mortality (AUROC 0.744 versus 0.686). The EM alone also outperformed the CCI (0.696 versus 0.601). Capturing comorbidities over a prolonged period only modestly improved the predictive value of either index: EM 1-year look-back 0.645 versus 5-year 0.676 versus complete record 0.695 and CCI 0.574 versus 0.591 versus 0.605. CONCLUSIONS: The comorbidity code lists may be used by future researchers to calculate CCI and EM using records from Read coded databases. The EM is preferable to the CCI but only marginal gains should be expected from incorporating comorbidities over a period longer than 1 year. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0753-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-06 /pmc/articles/PMC6554904/ /pubmed/31170931 http://dx.doi.org/10.1186/s12874-019-0753-5 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Metcalfe, David
Masters, James
Delmestri, Antonella
Judge, Andrew
Perry, Daniel
Zogg, Cheryl
Gabbe, Belinda
Costa, Matthew
Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases
title Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases
title_full Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases
title_fullStr Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases
title_full_unstemmed Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases
title_short Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases
title_sort coding algorithms for defining charlson and elixhauser co-morbidities in read-coded databases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6554904/
https://www.ncbi.nlm.nih.gov/pubmed/31170931
http://dx.doi.org/10.1186/s12874-019-0753-5
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