The Elixhauser Comorbidity Method Outperforms the Charlson Index in Predicting Inpatient Death After Orthopaedic Surgery

Clinical Orthopaedics and Related Research® volume 472, pages2878–2886(2014)Cite this article

Abstract

Background

Scores derived from comorbidities can help with risk adjustment of quality and safety data. The Charlson and Elixhauser comorbidity measures are well-known risk adjustment models, yet the optimal score for orthopaedic patients remains unclear.

Questions/purposes

We determined whether there was a difference in the accuracy of the Charlson and Elixhauser comorbidity-based measures in predicting (1) in-hospital mortality after major orthopaedic surgery, (2) in-hospital adverse events, and (3) nonroutine discharge.

Methods

Among an estimated 14,007,813 patients undergoing orthopaedic surgery identified in the National Hospital Discharge Survey (1990–2007), 0.80% died in the hospital. The association of each Charlson comorbidity measure and Elixhauser comorbidity measure with mortality was assessed in bivariate analysis. Two main multivariable logistic regression models were constructed, with in-hospital mortality as the dependent variable and one of the two comorbidity-based measures (and age, sex, and year of surgery) as independent variables. A base model that included only age, sex, and year of surgery also was evaluated. The discriminative ability of the models was quantified using the area under the receiver operating characteristic curve (AUC). The AUC quantifies the ability of our models to assign a high probability of mortality to patients who die. Values range from 0.50 to 1.0, with 0.50 indicating no ability to discriminate and 1.0 indicating perfect discrimination.

Results

Elixhauser comorbidity adjustment provided a better prediction of in-hospital case mortality (AUC, 0.86; 95% CI, 0.86–0.86) compared with the Charlson model (AUC, 0.83; 95% CI, 0.83–0.84) and to the base model with no comorbidities (AUC, 0.81; 95% CI, 0.81–0.81). In terms of relative improvement in predictive performance, the Elixhauser measure performed 60% better than the Charlson score in predicting mortality. The Elixhauser model discriminated inpatient morbidity better than the Charlson measure, but the discriminative ability of the model was poor and the difference in the absolute improvement in predictive power between the two models (AUC, 0.01) is of dubious clinical importance. Both comorbidity models exhibited the same degree of discrimination for estimating nonroutine discharge (AUC, 0.81; 95% CI, 0.81–0.82 for both models).

Conclusions

Provider-specific outcomes, particularly inpatient mortality, may be evaluated differently depending on the comorbidity risk adjustment model selected. Future research assessing and comparing the performance of the Charlson and Elixhauser measures in predicting long-term outcomes would be of value.

Level of Evidence

Level II, prognostic study. See the Instructions for Authors for a complete description of levels of evidence.

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Author information

Affiliations

  1. Orthopaedic Hand and Upper Extremity Service, Massachusetts General Hospital, Yawkey Center, Suite 2100, 55 Fruit Street, Boston, MA, 02114, USA

    Mariano E. Menendez MD, Valentin Neuhaus MD & David Ring MD, PhD

  2. Department of Orthopaedic Surgery, Academic Medical Center, Amsterdam, The Netherlands

    C. Niek van Dijk MD, PhD

Authors
  1. Mariano E. Menendez MD
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  2. Valentin Neuhaus MD
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  3. C. Niek van Dijk MD, PhD
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  4. David Ring MD, PhD
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Corresponding author

Correspondence to Mariano E. Menendez MD.

Additional information

Each author certifies that he or she, or a member of his or her immediate family, has no funding or commercial associations (eg, consultancies, stock ownership, equity interest, patent/licensing arrangements, etc) that might pose a conflict of interest in connection with the submitted article.

All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research ® editors and board members are on file with the publication and can be viewed on request.

Each author certifies that his or her institution waived approval for the human protocol for this investigation and that all investigations were conducted in conformity with ethical principles of research.

This work was performed at the Orthopaedic Hand and Upper Extremity Service, Massachusetts General Hospital, Boston, MA, USA.

Appendix

Appendix

List of ICD-9 codes included to identify adverse events

Adverse events ICD-9 codes
Postoperative shock 998.0
Hematoma or seroma 998.1
Disruption of wound 998.3
Postoperative infection 998.5
Acute posthemorrhagic anemia 285.1
Complications not elsewhere classified 997
Acute renal failure 580, 584
Acute myocardial infarction 410
Ventricular arrhythmias and cardiac arrest 427.4, 427.5
Pneumonia and pulmonary congestion 482, 485, 486, 514, 5184
Iatrogenic postoperative hypotension 458.29
Pulmonary embolism 415.1
Induced mental disorders 291, 292, 293
Cerebrovascular disease 430, 431, 432, 433, 434, 435, 436
Fat embolism 958.1
Pulmonary insufficiency after trauma, surgery, transfusion, or acute respiratory distress syndrome 518.5, 518.7, 518.81
Thrombotic events 451.1, 451.2, 453.2, 453.4, 453.8, 453.9
Intubation and mechanical ventilation 96.04, 96.07
Transfusion of blood (components) 99.0
Conversion of cardiac rhythm 99.6
  1. ICD-9 = International Classification of Diseases, 9th Revision.

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Menendez, M.E., Neuhaus, V., van Dijk, C.N. et al. The Elixhauser Comorbidity Method Outperforms the Charlson Index in Predicting Inpatient Death After Orthopaedic Surgery. Clin Orthop Relat Res 472, 2878–2886 (2014). https://doi.org/10.1007/s11999-014-3686-7

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Keywords

  • Inpatient Mortality
  • Major Orthopaedic Surgery
  • National Hospital Discharge Survey
  • Risk Adjustment Model
  • Elixhauser Comorbidity