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dc.contributor.authorGhali, William A.
dc.contributor.authorHall, Ruth E.
dc.contributor.authorRosen, Amy K.
dc.contributor.authorAsh, Arlene S.
dc.contributor.authorMoskowitz, Mark A.
dc.date2022-08-11T08:10:41.000
dc.date.accessioned2022-08-23T17:16:56Z
dc.date.available2022-08-23T17:16:56Z
dc.date.issued1996-03-01
dc.date.submitted2010-07-01
dc.identifier.citationJ Clin Epidemiol. 1996 Mar;49(3):273-8. <a href="http://dx.doi.org/10.1016/0895-4356(95)00564-1">Link to article on publisher's site</a>
dc.identifier.issn0895-4356 (Linking)
dc.identifier.doi10.1016/0895-4356(95)00564-1
dc.identifier.pmid8676173
dc.identifier.urihttp://hdl.handle.net/20.500.14038/47512
dc.description.abstractWe studied approaches to comorbidity risk adjustment by comparing two ICD-9-CM adaptations (Deyo, Dartmouth-Manitoba) of the Charlson comorbidity index applied to Massachusetts coronary artery bypass surgery data. We also developed a new comorbidity index by assigning study-specific weights to the original Charlson comorbidity variables. The 2 ICD-9-CM coding adaptations assigned identical Charlson comorbidity scores to 90% of cases, and specific comorbidities were largely found in the same cases (kappa values of 0.72-1.0 for 15 of 16 comorbidities). Meanwhile, the study-specific comorbidity index identified a 10% subset of patients with 15% mortality, whereas the 5% highest-risk patients according to the Charlson index had only 8% mortality (p = 0.01). A model using the new index to predict mortality had better validated performance than a model based on the original Charlson index (c = 0.74 vs. 0.70). Thus, in our population, the ICD-9-CM adaptation used to create the Charlson score mattered little, but using study-specific weights with the Charlson variables substantially improved the power of these data to predict mortality.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=8676173&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://dx.doi.org/10.1016/0895-4356(95)00564-1
dc.subjectAged
dc.subjectComorbidity
dc.subjectCoronary Artery Bypass
dc.subjectCoronary Disease
dc.subjectFemale
dc.subjectHospital Mortality
dc.subjectHumans
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectModels, Statistical
dc.subjectMultivariate Analysis
dc.subjectReproducibility of Results
dc.subjectBiostatistics
dc.subjectEpidemiology
dc.subjectHealth Services Research
dc.titleSearching for an improved clinical comorbidity index for use with ICD-9-CM administrative data
dc.typeJournal Article
dc.source.journaltitleJournal of clinical epidemiology
dc.source.volume49
dc.source.issue3
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/648
dc.identifier.contextkey1378794
html.description.abstract<p>We studied approaches to comorbidity risk adjustment by comparing two ICD-9-CM adaptations (Deyo, Dartmouth-Manitoba) of the Charlson comorbidity index applied to Massachusetts coronary artery bypass surgery data. We also developed a new comorbidity index by assigning study-specific weights to the original Charlson comorbidity variables. The 2 ICD-9-CM coding adaptations assigned identical Charlson comorbidity scores to 90% of cases, and specific comorbidities were largely found in the same cases (kappa values of 0.72-1.0 for 15 of 16 comorbidities). Meanwhile, the study-specific comorbidity index identified a 10% subset of patients with 15% mortality, whereas the 5% highest-risk patients according to the Charlson index had only 8% mortality (p = 0.01). A model using the new index to predict mortality had better validated performance than a model based on the original Charlson index (c = 0.74 vs. 0.70). Thus, in our population, the ICD-9-CM adaptation used to create the Charlson score mattered little, but using study-specific weights with the Charlson variables substantially improved the power of these data to predict mortality.</p>
dc.identifier.submissionpathqhs_pp/648
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages273-8


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