Comparison of the Elixhauser and Charlson/Deyo Methods of Comorbidity Measurement in Administrative Data

BACKGROUND: Comorbidity risk adjustment methods have been used widely with administrative data, and the Charlson/Deyo method is perhaps the most commonly used in the literature. However, a new method defined by Elixhauser et al. has been introduced recently and could be superior, although it has not been validated widely. OBJECTIVES: We compared the Charlson/Deyo and Elixhauser methods using Canadian administrative data on patients with myocardial infarction (MI). RESEARCH DESIGN: We conducted a historical cohort study. SUBJECTS: We used administrative hospital discharge data from a large Canadian city for all cases with acute MI coded as most responsible diagnosis between January 1, 1995, and March 31, 2001. MEASURES: We used each of the 2 methods to define comorbidity variables based on the International Classification of Diseases, 9th Revision, Clinical Modification codes present in each case record. We then compared 2 models predicting in-hospital mortality based on presence or absence of the variables defined by each of the methods. Frequency tables were produced and c-statistics and changes in -2 log likelihood (-2LogL) were calculated. We also visually assessed model performance by plotting observed and expected percentages of death for increasing risk categories defined by the 2 models. RESULTS: The Elixhauser model outperformed the Charlson/Deyo model in predicting mortality, with higher c-statistic values (0.793 vs. 0.704). Superior performance of the Elixhauser method is confirmed when plotting the expected and observed risks of death across groupings of increasing risk, in which the Elixhauser method yields a wider range of predicted and observed probabilities of death across groupings (2.5%-33%) than does the Charlson/Deyo method (5%-25%). CONCLUSIONS: The Elixhauser comorbidity measurement method performs better than the widely used Charlson/Deyo method in the Canadian acute MI cases studied

Comorbidity · Myocardial infarction · Statistic · Statistics · Acute Myocardial Infarction Research · Chronic Disease Management Strategies · Emergency Medicine · Internal Medicine · Mathematics · Medicine · Sepsis Diagnosis and Treatment

• A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach

  Open Access•Md Ekramul Hossain, Shahadat Uddin et al.•International Journal of…•2020

  Incluir item

• Tetanus Vaccination and Extra-Immunization among Adult Populations

  Open Access•Shih-Wei Liu, Liang-Chung Huang et al.•International Journal of…•2018

  Incluir item

• Ethnic Differences in Geriatric Conditions and Diabetes Complications Among Older, Insured Adults With Diabetes

  Open Access•Andrew J Karter, Neda Laiteerapong et al.•Journal of Aging and Health•2015

  Incluir item

• Trends in Hospital Mortality for Uninsured Rural and Urban Populations, 2012‐2016

  Open Access•Lauren E Elson, Lauren Elson et al.•The Journal of Rural Health•2021

  Incluir item

• Systematic Review of Comorbidity Indices for Administrative Data

  Mansour T A Sharabiani, Paul Aylin et al.•Medical Care•2012

  Incluir item

• The association of socioeconomic disadvantage with postoperative complications after major elective cardiovascular surgery

  Open Access•Nera Agabiti, Giulia Cesaroni et al.•Journal of Epidemiology and…•2008

  Incluir item

• A Comparison of In-hospital Mortality Risk Conferred by High Hospital Occupancy, Differences in Nurse Staffing Levels, Weekend Admission, and Seasonal Influenza

  Peter L Schilling, Darrell A Campbell et al.•Medical Care•2010

  Incluir item

• Empirically Derived Composite Measures of Surgical Performance

  Douglas O Staiger, Justin B Dimick et al.•Medical Care•2009

  Incluir item

• Incorporating the Present-on-Admission Indicator to Predict In-hospital Mortality Through Elixhauser Measures

  Jianfang Liu, Ani Bilazarian et al.•Medical Care•2025

  Incluir item

• Mortality Risk Prediction

  Benjamin P Chapman, Alexander Weiss et al.•Medical Care•2015

  Incluir item

• In Search of the Perfect Comorbidity Measure for Use With Administrative Claims Data

  Laura-Mae Baldwin, Laura–Mae Baldwin et al.•Medical Care•2006

  Incluir item

• Is Lower 30-Day Mortality Posthospital Admission Among Blacks Unique to the Veterans Affairs Health Care System

  Daniel Polsky, Judith R Lave et al.•Medical Care•2007

  Incluir item

• Predicting the Cost of Health Care Services

  Xiaotong Huang, Sandra Peterson et al.•Medical Care•2020

  Incluir item

• Organization of Hospital Nursing and 30-Day Readmissions in Medicare Patients Undergoing Surgery

  Chenjuan Ma, Matthew D McHugh et al.•Medical Care•2015

  Incluir item

• The Fragmentation of Hospital Use Among a Cohort of High Utilizers

  Katherine Hempstead, Derek DeLia et al.•Medical Care•2014

  Incluir item

• Should We Add Clinical Variables to Administrative Data

  Trisha C Johnston, Trisha Johnston et al.•Medical Care•2007

  Incluir item

• Who Will be the Costliest Patients? Using Recent Claims to Predict Expensive Surgical Episodes

  Karan R Chhabra, Ushapoorna Nuliyalu et al.•Medical Care•2019

  Incluir item

• Evaluation of the Effectiveness of a Surgical Checklist in Medicare Patients

  Bradley N Reames, Christopher P Scally et al.•Medical Care•2015

  Incluir item

• The Relationships Among Physician and Hospital Volume, Processes, and Outcomes of Care for Acute Myocardial Infarction

  Yu-Chi Tung, Yu‐Chi Tung et al.•Medical Care•2014

  Incluir item

• Healthcare Utilization and Outcomes After Bariatric Surgery

  William Encinosa, William E Encinosa et al.•Medical Care•2006

  Incluir item

• Mental Comorbidity and Quality of Diabetes Care Under Medicaid

  Benjamin G Druss, Liping Zhao et al.•Medical Care•2012

  Incluir item

• A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data

  Carl van Walraven, Peter C Austin et al.•Medical Care•2009

  Incluir item

• A New Elixhauser-based Comorbidity Summary Measure to Predict In-Hospital Mortality

  Nicolas R Thompson, Youran Fan et al.•Medical Care•2015

  Incluir item

• Mortality Among Older Adults Before Versus After Hospital Transition to Intensivist Staffing

  Myura Nagendran, Justin B Dimick et al.•Medical Care•2016

  Incluir item

• Emergency Department Crowding Predicts Admission Length-of-Stay But Not Mortality in a Large Health System

  Stephen F Derose, Gelareh Z Gabayan et al.•Medical Care•2014

  Incluir item

• Presentation adapting a clinical comorbidity index for use with ICD-9-CM administrative data

  Open Access•Patrick S Romano, L L Roos et al.•Journal of Clinical Epidemiology•1993

  Incluir item

• Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

  Open Access•Richard A Deyo, R DEYO•Journal of Clinical Epidemiology•1992

  Incluir item

• A new method of classifying prognostic comorbidity in longitudinal studies

  Open Access•Mary E Charlson, Peter Pompei et al.•Journal of Chronic Diseases•1987

  Incluir item

• Comorbidity Measures for Use with Administrative Data

  Anne Elixhauser, Claudia Steiner et al.•Medical Care•1998

  Incluir item

• Comparison of the Performance of Two Comorbidity Measures, With and Without Information From Prior Hospitalizations

  George J Stukenborg, Douglas P Wagner et al.•Medical Care•2001

  Incluir item