BACKGROUND: Understanding comorbidity prevalence and the effects of comorbidities in older veterans with lower extremity amputations may aid in assessing patient outcomes, resource use, and facility-level quality of care. OBJECTIVES: To determine the degree to which adding outpatient to inpatient administrative data sources yields higher comorbidity prevalence estimates and improved explanatory power of models predicting 1-year mortality and to compare the Charlson/Deyo and Elixhauser comorbidity measures. METHODS: A retrospective cohort study applying frequencies, cross-tabulations, and logistic regression models was conducted, including data from 2,375 veterans with lower extremity amputations. Comorbidity prevalence according to the Charlson/Deyo and Elixhauser measures, 1-year mortality rates, and standardized mortality ratios (SMRs) were analyzed. RESULTS: Comorbidity prevalence estimates increased sharply for both the Charlson/Deyo and Elixhauser measures with the addition of data from multiple settings. The Elixhauser compared to the Charlson/Deyo generally yielded higher estimates but did not improve explanatory power for mortality. Modeling expected versus actual deaths produced varying SMRs across geographic regions but was not dependent on which measure or data sources were used. CONCLUSIONS: Merging outpatient with inpatient data may reduce the under coding of comorbidities but does not enhance mortality prediction. Compared to the Charlson/Deyo, the Elixhauser has a more complete coding scheme for comorbid conditions, such as diabetes mellitus and peripheral vascular disease, important to addressing lower extremity amputation etiology.
BACKGROUND: Understanding comorbidity prevalence and the effects of comorbidities in older veterans with lower extremity amputations may aid in assessing patient outcomes, resource use, and facility-level quality of care. OBJECTIVES: To determine the degree to which adding outpatient to inpatient administrative data sources yields higher comorbidity prevalence estimates and improved explanatory power of models predicting 1-year mortality and to compare the Charlson/Deyo and Elixhauser comorbidity measures. METHODS: A retrospective cohort study applying frequencies, cross-tabulations, and logistic regression models was conducted, including data from 2,375 veterans with lower extremity amputations. Comorbidity prevalence according to the Charlson/Deyo and Elixhauser measures, 1-year mortality rates, and standardized mortality ratios (SMRs) were analyzed. RESULTS: Comorbidity prevalence estimates increased sharply for both the Charlson/Deyo and Elixhauser measures with the addition of data from multiple settings. The Elixhauser compared to the Charlson/Deyo generally yielded higher estimates but did not improve explanatory power for mortality. Modeling expected versus actual deaths produced varying SMRs across geographic regions but was not dependent on which measure or data sources were used. CONCLUSIONS: Merging outpatient with inpatient data may reduce the under coding of comorbidities but does not enhance mortality prediction. Compared to the Charlson/Deyo, the Elixhauser has a more complete coding scheme for comorbid conditions, such as diabetes mellitus and peripheral vascular disease, important to addressing lower extremity amputation etiology.
Authors: Chin-Lin Tseng; Mangala Rajan; Donald R Miller; Gerald Hawley; Stephen Crystal; Minge Xie; Anjali Tiwari; Monika Safford; Leonard Pogach Journal: Med Care Date: 2005-01 Impact factor: 2.983
Authors: Margaret G Stineman; Pui L Kwong; Dawei Xie; Jibby E Kurichi; Diane Cowper Ripley; David M Brooks; Douglas E Bidelspach; Barbara E Bates Journal: PM R Date: 2010-04 Impact factor: 2.298
Authors: R Sever; S Sherman; N Rozen; O Chechik; S Hilu; S Abdelrazek; M Salai; D Justo Journal: Z Gerontol Geriatr Date: 2014-09-20 Impact factor: 1.281
Authors: Barbara E Bates; Richard Hallenbeck; Toni Ferrario; Pui L Kwong; Jibby E Kurichi; Margaret G Stineman; Dawei Xie Journal: PM R Date: 2012-08-30 Impact factor: 2.298
Authors: Jianxun Zhou; Barbara E Bates; Jibby E Kurichi; Pui L Kwong; Dawei Xie; Margaret G Stineman Journal: Arch Phys Med Rehabil Date: 2011-09 Impact factor: 3.966
Authors: Jibby E Kurichi; Dylan S Small; Barbara E Bates; Janet A Prvu-Bettger; Pui L Kwong; W Bruce Vogel; Douglas E Bidelspach; Margaret G Stineman Journal: Med Care Date: 2009-04 Impact factor: 2.983
Authors: Barbara E Bates; Pui L Kwong; Jibby E Kurichi; Douglas E Bidelspach; Dean M Reker; Greg Maislin; Dawei Xie; Margaret Stineman Journal: Arch Phys Med Rehabil Date: 2009-12 Impact factor: 3.966
Authors: Jibby E Kurichi; Diane Cowper Ripley; Dawei Xie; Pui L Kwong; Barbara E Bates; Margaret G Stineman Journal: PM R Date: 2012-11-14 Impact factor: 2.298
Authors: Margaret G Stineman; Pui L Kwong; Jibby E Kurichi; Janet A Prvu-Bettger; W Bruce Vogel; Greg Maislin; Barbara E Bates; Dean M Reker Journal: Arch Phys Med Rehabil Date: 2008-10 Impact factor: 3.966