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

• Published in: Clinical orthopaedics and related research 2014-09, Vol.472 (9), p.2878-2886
• Main Authors: Menendez, Mariano E., Neuhaus, Valentin, van Dijk, C. Niek, Ring, David
• Format: Article
• Language: English
• Subjects: Aged; Aged, 80 and over; Bone Diseases - epidemiology; Bone Diseases - surgery; Clinical Research; Comorbidity; Conservative Orthopedics; Female; Hospital Mortality - trends; Humans; Inpatients - statistics & numerical data; Male; Medicine; Medicine & Public Health; Middle Aged; Orthopedic Procedures - mortality; Orthopedics; Patient Discharge - trends; Prognosis; Retrospective Studies; Risk Assessment - methods; ROC Curve; Sports Medicine; Surgery; Surgical Orthopedics; United States - epidemiology
• ISSN: 0009-921X; 1528-1132
• DOI: 10.1007/s11999-014-3686-7

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