Impact of different measures of comorbid disease on predicted mortality of intensive care unit patients

Joseph A. Johnston, Douglas P. Wagner, Stephen Timmons, Deborah Welsh, Joel Tsevat, Marta L. Render

Research output: Contribution to journalArticle

72 Citations (Scopus)

Abstract

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 Hixhauser, 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 univariale and multivariable predictors of inhospital mortality, respectively. In a multivariable model including all available predictors, comorbidity accounted for less (8.4%) of the model's uniquely attributable χ2 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.

Original languageEnglish (US)
Pages (from-to)929-940
Number of pages12
JournalMedical Care
Volume40
Issue number10
DOIs
StatePublished - Oct 1 2002
Externally publishedYes

Fingerprint

Intensive Care Units
Comorbidity
APACHE
Mortality
Hospital Mortality
Hospitalization
Logistic Models
Risk Adjustment
Cost of Illness
International Classification of Diseases
Veterans
Length of Stay
Chronic Disease
Cohort Studies
Multivariate Analysis
Retrospective Studies
Databases
Survival

Keywords

  • Comorbidity
  • Intensive care
  • Mortality prediction
  • Risk adjustment

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Impact of different measures of comorbid disease on predicted mortality of intensive care unit patients. / Johnston, Joseph A.; Wagner, Douglas P.; Timmons, Stephen; Welsh, Deborah; Tsevat, Joel; Render, Marta L.

In: Medical Care, Vol. 40, No. 10, 01.10.2002, p. 929-940.

Research output: Contribution to journalArticle

Johnston, Joseph A. ; Wagner, Douglas P. ; Timmons, Stephen ; Welsh, Deborah ; Tsevat, Joel ; Render, Marta L. / Impact of different measures of comorbid disease on predicted mortality of intensive care unit patients. In: Medical Care. 2002 ; Vol. 40, No. 10. pp. 929-940.
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N2 - 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 Hixhauser, 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 univariale and multivariable predictors of inhospital mortality, respectively. In a multivariable model including all available predictors, comorbidity accounted for less (8.4%) of the model's uniquely attributable χ2 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.

AB - 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 Hixhauser, 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 univariale and multivariable predictors of inhospital mortality, respectively. In a multivariable model including all available predictors, comorbidity accounted for less (8.4%) of the model's uniquely attributable χ2 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.

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