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

Abstract

Background Scores derived from comorbidities can help with risk adjustment of quality and safety data. The Charlson and Elixhauser comorbidity measures are wellknown 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 (1990Survey ( -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, 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

Who Leaves the Hospital Against Medical Advice in the Orthopaedic Setting?

“…Baseline comorbidity status was assessed using the Elixhauser comorbidity algorithm, which contains 30 comorbidities [12,31,35]. Four conditions included in the Elixhauser algorithm (alcohol abuse, drug abuse, psychosis, and AIDS/HIV infection) were analyzed separately, because they have been previously associated with self-discharge [22,32], thus leaving 26 comorbidities for summation (Table 1).…”

A Comparison of the Charlson and Elixhauser Comorbidity Measures to Predict Inpatient Mortality After Proximal Humerus Fracture

“…11 The Elixhauser comorbidity measure, a newer approach consisting of 30 conditions, has been identified as a better predictor of mortality in patients hospitalized for cardiac, oncologic, hepatobiliary, and gastrointestinal disease and more recently among patients undergoing major orthopaedic surgery. [16][17][18][19][20][21] Several comorbidities that are highly prevalent in orthopaedic trauma patients are encompassed in one but not the other risk adjustment method. For instance, hypertension, obesity, weight loss, and depression are not included in the Charlson index, whereas stroke and myocardial infarction are not contained in the Elixhauser model.…”

“…For instance, hypertension, obesity, weight loss, and depression are not included in the Charlson index, whereas stroke and myocardial infarction are not contained in the Elixhauser model. 12,20 Using a large healthcare utilization database, we sought to assess and compare the discriminative ability of the Charlson and Elixhauser comorbidity-based measures for predicting inpatient mortality after proximal humerus fracture.…”

Association of perioperative blood transfusion and adverse events after operative treatment of proximal humerus fractures

“…The explanatory variables were age (both continuous and categorised into the age groups: <60, 60-79, and !80 years), gender, race/ethnicity (white, black, Hispanic, and other), insurance status (private insurance, public insurance, and uninsured), household income based on postal area code analysis ($1-$38,999, $39,000-$47,999, $48,000-$62,999, and !$63,000), length of hospitalisation, discharge disposition (home, home health care, rehabilitation/skilled nursing facility, hospital transfer, and other), hospital location (urban and rural), and hospital academic affiliation (nonteaching and teaching). The baseline comorbidity status was assessed using the Elixhauser comorbidity algorithm, which contains 30 comorbidities, including chronic anaemia and coagulopathy [7][8][9]. The mean age in the study sample was 68 AE 16 years.…”