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Cross-National Comparative Performance of Three Versions of the ICD-10 Charlson Index

Dados Bibliográficos

ID9099503
AutoresVijaya Sundararajan (0000-0001-9387-1865, The Royal Melbourne Hospital, autor correspondente), Hude Quan (0000-0002-7848-7256, University of Calgary), Patricia Halfon (Institute of Social and Preventive Medicine), Kiyohide Fushimi (0000-0002-1894-0290, Tokyo Medical and Dental University), Jean-Christophe Luthi, Jean‐Christophe Luthi (Institute of Social and Preventive Medicine), Bernard Burnand (0000-0002-5678-6044, Institute of Social and Preventive Medicine), William A Ghali (University of Calgary, autor correspondente)
Ano2007
Volume45
Fascículo12
Páginas1210-1215
Data de publicação2007-12-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e3181484347
PMID18007172
OpenAlexW2044184365
IdiomaEN
Citações recebidas11
Referências citadas13

OBJECTIVE: The Charlson comorbidity index has been widely used for risk adjustment in outcome studies using administrative health data. Recently, 3 International Statistical Classification of Diseases, Tenth Revision (ICD-10) translations have been published for the Charlson comorbidities. This study was conducted to compare the predictive performance of these versions (the Halfon, Sundararajan, and Quan versions) of the ICD-10 coding algorithms using data from 4 countries. METHODS: Data from Australia (N = 2000-2001, max 25 diagnosis codes), Canada (N = 2002-2003, max 16 diagnosis codes), Switzerland (N = 1999-2001, unlimited number of diagnosis codes), and Japan (N = 2003, max 11 diagnosis codes) were analyzed. Only the first admission for patients age 18 years and older, with a length of stay of >/=2 days was included. For each algorithm, 2 logistic regression models were fitted with hospital mortality as the outcome and the Charlson individual comorbidities or the Charlson index score as independent variables. The c-statistic (representing the area under the receiver operating characteristic curve) and its 95% probability bootstrap distribution were employed to evaluate model performance. RESULTS: Overall, within each population's data, the distribution of comorbidity level categories was similar across the 3 translations. The Quan version produced slightly higher median c-statistics than the Halfon or Sundararajan versions in all datasets. For example, in Japanese data, the median c-statistics were 0.712 (Quan), 0.709 (Sundararajan), and 0.694 (Halfon) using individual comorbidity coefficients. In general, the probability distributions between the Quan and the Sundararajan versions overlapped, whereas those between the Quan and the Halfon version did not. CONCLUSIONS: Our analyses show that all of the ICD-10 versions of the Charlson algorithm performed satisfactorily (c-statistics 0.70-0.86), with the Quan version showing a trend toward outperforming the other versions in all data sets

Charlson comorbidity index · Comorbidity · Diagnosis code · Index (typography) · Logistic regression · Population · Receiver operating characteristic · Statistic · Statistics · Chronic Disease Management Strategies · Computer Science · Demography · Internal Medicine · Mathematics · Medical Coding and Health Information · Medicine · Primary Care and Health Outcomes

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Obras citantes distintas11
Citações por ano0,69
Intervalo de citações2010 - 2020 (11)
Velocidade de citaçãohistorical
Altamente citadoNão
Tipos de citaçãoNeutras: 10
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