BACKGROUND: Valid comparison of patient survival across ICUs requires adjustment for burden of chronic illness. The optimal measure of comorbidity in this setting remains uncertain. OBJECTIVES: To examine the impact of different measures of comorbid disease on predicted mortality for ICU patients. DESIGN: Retrospective cohort study. SUBJECTS: Seventeen thousand eight hundred ninety-three veterans from 17 geographically diverse VA Medical Centers and 43 ICUs were studied, admitted between February 1, 1996 and July 31, 1997. MEASURES: ICD-9-CM codes reflecting comorbid disease from hospital stays before and including the index hospitalization from local VA computer databases were extracted, and three measures of comorbid disease were then compared: (1) an APACHE-weighted comorbidity score using comorbid diseases used in APACHE, (2) a count of conditions described by Elixhauser, and (3) Elixhauser comorbid diseases weighted independently. Univariate analyses and multivariate logistic regression models were used to determine the contribution of each measure to in-hospital mortality predictions. RESULTS: Models using independently weighted Elixhauser comorbidities discriminated better than models using an APACHE-weighted score or a count of Elixhauser comorbidities. Twenty-three and 14 of the Elixhauser conditions were significant univariate and multivariable predictors of in-hospital mortality, respectively. In a multivariable model including all available predictors, comorbidity accounted for less (8.4%) of the model's uniquely attributable chi statistic than laboratory values (67.7%) and diagnosis (17.7%), but more than age (4.0%) and admission source (2.1%). Excluding codes from prior hospitalizations did not adversely affect model performance. CONCLUSIONS: Independently weighted comorbid conditions identified through computerized discharge abstracts can contribute significantly to ICU risk adjustment models.
BACKGROUND: Valid comparison of patient survival across ICUs requires adjustment for burden of chronic illness. The optimal measure of comorbidity in this setting remains uncertain. OBJECTIVES: To examine the impact of different measures of comorbid disease on predicted mortality for ICU patients. DESIGN: Retrospective cohort study. SUBJECTS: Seventeen thousand eight hundred ninety-three veterans from 17 geographically diverse VA Medical Centers and 43 ICUs were studied, admitted between February 1, 1996 and July 31, 1997. MEASURES: ICD-9-CM codes reflecting comorbid disease from hospital stays before and including the index hospitalization from local VA computer databases were extracted, and three measures of comorbid disease were then compared: (1) an APACHE-weighted comorbidity score using comorbid diseases used in APACHE, (2) a count of conditions described by Elixhauser, and (3) Elixhauser comorbid diseases weighted independently. Univariate analyses and multivariate logistic regression models were used to determine the contribution of each measure to in-hospital mortality predictions. RESULTS: Models using independently weighted Elixhauser comorbidities discriminated better than models using an APACHE-weighted score or a count of Elixhauser comorbidities. Twenty-three and 14 of the Elixhauser conditions were significant univariate and multivariable predictors of in-hospital mortality, respectively. In a multivariable model including all available predictors, comorbidity accounted for less (8.4%) of the model's uniquely attributable chi statistic than laboratory values (67.7%) and diagnosis (17.7%), but more than age (4.0%) and admission source (2.1%). Excluding codes from prior hospitalizations did not adversely affect model performance. CONCLUSIONS: Independently weighted comorbid conditions identified through computerized discharge abstracts can contribute significantly to ICU risk adjustment models.
Authors: Dustin D French; Thomas N Chirikos; Andrea Spehar; Robert Campbell; Heidi Means; Tatjana Bulat Journal: Drug Saf Date: 2005 Impact factor: 5.606
Authors: Sean M Bagshaw; H Thomas Stelfox; Robert C McDermid; Darryl B Rolfson; Ross T Tsuyuki; Nadia Baig; Barbara Artiuch; Quazi Ibrahim; Daniel E Stollery; Ella Rokosh; Sumit R Majumdar Journal: CMAJ Date: 2013-11-25 Impact factor: 8.262
Authors: Ulas D Bayraktar; Elizabeth J Shpall; Ping Liu; Stefan O Ciurea; Gabriela Rondon; Marcos de Lima; Marylou Cardenas-Turanzas; Kristen J Price; Richard E Champlin; Joseph L Nates Journal: J Clin Oncol Date: 2013-10-14 Impact factor: 44.544
Authors: Mercedes Falciglia; Ron W Freyberg; Peter L Almenoff; David A D'Alessio; Marta L Render Journal: Crit Care Med Date: 2009-12 Impact factor: 7.598
Authors: Jennifer Hornung Garvin; Andrew Redd; Dan Bolton; Pauline Graham; Dominic Roche; Peter Groeneveld; Molly Leecaster; Shuying Shen; Mark G Weiner Journal: Perspect Health Inf Manag Date: 2013-10-01