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

Menendez, Mariano E., MD1,a; Neuhaus, Valentin, MD1; van Dijk, Niek C., MD, PhD2; Ring, David, MD, PhD1

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Clinical Orthopaedics and Related Research®: September 2014 - Volume 472 - Issue 9 - p 2878-2886
doi: 10.1007/s11999-014-3686-7
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Patients undergoing orthopaedic surgery often have multiple coexisting medical conditions (comorbidities) [13, 21, 24, 28, 39, 45, 58]. Preoperative risk assessment can help with decision-making and management strategies [11, 37, 50]. Several models for estimating risk based on coded comorbidities are currently in use for orthopaedic patients, but there is no consensus regarding the optimal approach [22, 37, 49, 51, 56, 57]. Selecting appropriate risk adjustment models can help hospitals contain costs while ensuring high levels of quality. Furthermore, inadequate comorbidity risk-adjustment might penalize practitioners and hospitals that care for the sickest patients [41].

Large administrative inpatient databases have been used to assess the effect of baseline comorbidity status on surgical care outcomes targeted in pay-for-performance initiatives such as mortality, morbidity, and discharge disposition [14, 22, 32-34, 37-39]. The Charlson Comorbidity Index [5] encompasses 19 medical conditions and is the most widely used comorbidity risk adjustment model in orthopaedic surgery. First reported in 1987 to predict 1-year mortality [5], this index subsequently was adapted for use with administrative databases [9]. The Elixhauser measure [12], a more recent model including 31 conditions, is believed to be a better predictor of mortality in patients with cardiac, gastrointestinal, hepatobiliary, and oncologic conditions [6, 18, 27, 52]. Several prevalent comorbidities such as hypertension, obesity, weight loss, and psychiatric disorders that are included in the Elixhauser model are not included in the Charlson model [12].

We therefore 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.

Patients and Methods

All data were extracted from the National Hospital Discharge Survey (NHDS) database [4, 8]. The NHDS is an annual probability sample survey of discharges from nonfederal, general, and short-stay hospitals in the United States [8, 19]. Sample data then were weighted to produce annual estimates of inpatient care [8]. A maximum of seven medical diagnoses and four procedures were gathered and coded with the use of ICD-9-CM codes. Patient demographic information, hospital characteristics, and inpatient outcomes such as discharge disposition and hospital length of stay also were collected. Recognizing its utility to answer valuable clinical questions, the NHDS has been used extensively to analyze data associated with a wide range of diagnoses and procedures across different medical specialties [3, 25, 33, 34, 38, 44, 48]. Because of prior adequate data deidentification, our study was exempt from institutional review board approval.

Patients with a procedure code (ICD-9-CM) for primary TKA (81.84), primary THA (81.51), or spinal fusion (81.00 to 81.08) were included in the sample. Patients who underwent hip fracture (820.x) surgery also were included in the analysis. From a database with more than 500,000,000 patients treated between 1990 to 2007, an estimated 14,007,813 patients were identified and included in the analysis. The mean age of the patients was 66 ± 15 years, and female patients accounted for 62% of the study sample (Table 1). Overall, an estimated 0.80% of the included patients died during hospitalization. Mortality rates ranged from 0.30% for total joint arthroplasty and spinal fusion to 2.6% for operative treatment of hip fracture (Table 1).

Table 1
Table 1:
Characteristics of the study cohort (n = 14,007,813)

Comorbidity burden was quantified using validated Charlson (adaptation by Deyo et al. [8]) and Elixhauser coding algorithms available for ICD-9-CM codes [9, 12, 43]. Dichotomous variables indicating the presence or absence of each Charlson and Elixhauser comorbidity were created, and their associations with mortality were assessed in bivariate analysis using chi-square tests. In addition, the original Charlson and the weighted Elixhauser scores, developed by van Walraven et al. [55], were computed and further stratified into groups (0, 1-2, 3-4, ≥ 5 and < 0, 0, 1-4, ≥ 5, respectively). The Charlson weights assigned to each comorbidity range from +1 to +6, while the Elixhauser weights range from −7 to +12 [5, 55]. Comorbidity scores then can be calculated for each patient by summing the individual weights of all comorbidities. Continuous variables (age, days of care) of the stratified Charlson and Elixhauser groups were analyzed with ANOVA, and categorical data (sex, mortality, adverse events, discharge status) were analyzed with the chi-square test (Table 2). Chronic pulmonary disease (11%) and uncomplicated diabetes mellitus (11%) were the most frequently encountered comorbidities when using the Charlson/Deyo algorithm (Table 3). Among all 31 Elixhauser comorbidities, uncomplicated hypertension (38%) was the most prevalent condition, followed by chronic pulmonary disease (11%) and uncomplicated diabetes mellitus (11%) (Table 4).

Table 2
Table 2:
Charlson and Elixhauser comorbidity scores in the study cohort (n = 14,007,813)
Table 3
Table 3:
Bivariate analysis of Charlson comorbidities in the study cohort (n = 14,007,813)
Table 4
Table 4:
Bivariate analysis of Elixhauser comorbidities (n = 14,007,813)

Multivariable binary logistic regression analyses were performed to assess the contributions of the individual Charlson (Table 5) and Elixhauser (Table 6) comorbidities to predicted in-hospital mortality, our primary response variable. Charlson and Elixhauser comorbidities with a p value less than 0.10 in bivariate analysis and present in at least 0.2 % of the population were included in the logistic regression modeling. Two main models were constructed; each of these regression models encompassed one of the two comorbidity-based scores, 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 [27].

Table 5
Table 5:
Logistic regression analysis of relation of Charlson comorbidities (n = 14,007,813)
Table 6
Table 6:
Logistic regression analysis of Elixhauser comorbidities (n = 14,007,813)

To determine which model best predicted inpatient mortality, receiver operating characteristic (ROC) curves were plotted and the regression models were compared on the basis of the area under the ROC curve (AUC) and its 95% CI [20, 54]. The AUC quantifies the ability of our models to assign a high probability of mortality to the patients who died [42]. Values range from 0.50 to 1.0, with 0.50 indicating no ability to discriminate and 1.0 indicating perfect discrimination. In general, values less than 0.70 are considered to show poor discrimination, values between 0.70 and 0.80 can be considered acceptable, and between 0.80 and 0.90 excellent. In addition to the absolute improvement in predictive performance, we calculated the difference between two AUCs in percent beyond the predictive power of the base model including age, sex, and year of surgery [47]. For instance, a difference in AUC between the Charlson and Elixhauser comorbidity scores of 0.80 and 0.90, when the baseline AUC is 0.75, corresponds to a 67% relative increase in AUC: 0.90-0.75 - 0.80-0.75/0.90-0.75 = 0.67. We calculated the relative improvement in predictive performance of the Elixhauser score to the Charlson score. Global model performance also was compared using the Nagelkerke pseudo R-square measure [35]. Our secondary outcome variables, in-hospital adverse events and nonroutine discharge, were analyzed in an analogous fashion to in-hospital mortality. In-hospital adverse events were defined by ICD-9 codes (Appendix), following a coding approach used in other studies [34, 38]. A nonroutine discharge was defined as discharge to a skilled nursing facility or rehabilitation center. So as to set stricter standards owing to the large weighted sample size, a p value less than 0.001 was used to define significance in all analyses.


Elixhauser comorbidity adjustment provided 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 the base model with no comorbidities (AUC, 0.81; 95% CI, 0.81-0.81) (Table 7). In terms of relative improvement in predictive ability, the Elixhauser model performed 60% better than the Charlson model. The base model already showed excellent discrimination and accounted for 12% of the variation (Nagelkerke R-square = 0.12). The further addition of the Charlson comorbidity score to the base model led to a 2% increase in the amount of variation explained. The inclusion of the Elixhauser score to the base model achieved greater discrimination than the base model alone and the model incorporating the Charlson index and explained 18% of the variability (Nagelkerke R-square = 0.18). The rates of in-hospital death, together with those of adverse events and nonroutine disposition, increased steadily with the number of comorbidities and index scores In multivariable logistic regression analysis, chronic renal failure (odds ratio [OR], 4.3; 95% CI, 4.2-4.4; p < 0.001), complicated diabetes mellitus (OR, 4.3; 95% CI, 4.2-4.5; p < 0.001), and myocardial infarction (OR, 3.9; 95% CI, 3.9-4.0; p < 0.001) were the Charlson conditions associated with the greatest odds of in-hospital death (Table 5). Weight loss (OR, 5.0; 95% CI, 4.8-5.1; p < 0.001), pulmonary circulation disorders (OR, 4.5; 95% CI, 4.4-4.7; p < 0.001), and chronic renal failure (OR, 4.4; 95% CI, 4.3-4.6; p < 0.001) had the highest adjusted odds of inpatient mortality in the Elixhauser algorithm (Table 6).

Table 7
Table 7:
Elixhauser and Charlson comorbidity method discrimination for inpatient outcomes after orthopaedic surgery

Although the Elixhauser measure (AUC, 0.65; 95% CI, 0.65-0.65) was 100% more accurate than the Charlson measure (AUC, 0.64; 95% CI, 0.64-0.64) in predicting adverse events in terms of relative improvement, 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 (Table 7).

The Elixhauser and Charlson models showed the same degree of discrimination for nonroutine discharge prediction after major orthopaedic surgery (AUC, 0.81; 95% CI, 0.81-0.81 for both scales) (Table 7).


Surgical mortality and morbidity rates are important parameters of in-hospital quality of care [11, 37, 50]. Given the increasing age and complexity of patients undergoing orthopaedic surgery, it is necessary to appropriately adjust for patient risk, recognizing that the underlying nature of some patients’ conditions may make them more likely than others to experience poor outcomes. We therefore assessed and compared the two most commonly used comorbidity risk adjustment models in orthopaedic surgery, the Charlson and Elixhauser measures, regarding their ability to predict in-hospital death, adverse events, and nonroutine discharge.

Our results should be interpreted after taking into account numerous factors. Despite access to large numbers and associated power, administrative databases have several recognized limitations [14, 23, 29]. First, the NHDS dataset is based on billing data from ICD-9-CM codes, and such a coding system may not fully capture the patient population of interest [1]. In particular, it has been suggested that administrative databases tend to underreport chronic medical conditions that are considered less acute in the perioperative orthopaedic surgery setting [16, 31, 33, 34, 39]. Second, the possibility of errors in coding of the diagnoses and procedures cannot be avoided [7]; however, misclassification mistakes distribute evenly in large-scale studies [53]. Third, the NHDS database does not include data regarding the timing of diagnoses, which hinders the differentiation of baseline comorbidities from complications [36]. Analyses of risk-adjusted mortality rates should adjust mortality rates only for baseline comorbid diseases, not complications that arise from surgery [15]. The degree to which this issue influenced our results is unclear, although it has been reported that the majority of common diagnoses are comorbidities rather than adverse events [15, 30, 36]. Fourth, the NHDS enabled only ascertainment of inpatient outcomes, and thus postdischarge complications and readmissions were not captured. Fifth, we performed risk-adjustment using administrative data only; inclusion of clinically relevant variables such as the American Society of Anesthesiologists (ASA) score or the Frailty Index developed by the Canadian Study of Health and Aging may have improved model performance [24, 40, 58]. Finally, we did not perform any clinical data abstraction from medical records, which is considered the gold standard risk adjustment method in these comparisons [10]; thus, we were able to compare the Charlson and Elixhauser measures only for their relative performance.

We found that the Elixhauser comorbidity risk adjustment model performed numerically better than the Charlson model in predicting in-hospital mortality after major orthopaedic surgery. Although differences in the AUC values between the two comorbidity-based measures were small, it has been noted that even slight improvements in the AUC for such indexes can translate into quantifiable reductions in confounding bias [46]. Overall, the AUC values for inpatient mortality for the Charlson and Elixhauser comorbidity-based measures in our study were comparable to or slightly higher than those described in other patient populations [6, 18, 27, 52]. Consistent with a study by Nikkel et al. [39] in patients with hip fractures, the Elixhauser weight loss or malnutrition comorbidity was the major factor influencing mortality. We found some comorbidities (eg, hypothyroidism, obesity, uncomplicated diabetes, hypertension) to be associated with decreased odds of inpatient mortality. It is counterintuitive that these comorbidities would protect against inpatient death. It may be that these comorbidities are most common in patients with less overall infirmity compared with the average orthopaedic inpatient.

The occurrence of in-hospital adverse events after major orthopaedic surgery was slightly more accurately predicted with the Elixhauser comorbidity index. However, in line with the study by Gordon et al. [17] looking at the influence of the Elixhauser and Charlson measures on reoperations after THA, the predictive accuracy of both models to detect adverse events was poor (AUC values < 0.70). An AUC value approximating 0.70 is considered acceptable for discrimination and validation of methods for ongoing use [18]; we therefore could not validate the Charlson or Elixhauser measures in terms of predicting perioperative complications after major orthopaedic surgery. There may be something beyond measurable comorbidities that is yet unaccounted for in orthopaedic inpatient morbidity.

Both comorbidity indexes provided clinically relevant insight for estimating nonroutine discharge after orthopaedic surgery; the Elixhauser score was no better than the Charlson score. This finding suggests that the Charlson and Elixhauser indexes are valid prediction tools for healthcare resource use risk adjustment, and researchers should choose between them based more on their availability and comfort with the method [2].

The Elixhauser measure has not been introduced in orthopaedic surgery research until recently [17, 26, 39, 57, 59, 60], perhaps because of scarce reported comparisons with the Charlson model [17] and concerns regarding the inclusion of too many explanatory variables (31 variables), therefore requiring a relatively large sample size [27]. The main attractiveness of the use of large administrative databases for medical research lies in the possibility of studying rare occurrence events, such as inpatient mortality, that otherwise would be difficult to investigate in small population studies [14].

Testing comorbidity risk adjustment measure performance in orthopaedic surgery is worthy of future study. Further research comparing the Charlson and Elixhauser methods with the less accessible (costwise) risk adjustment methods of Disease Staging (Thomson Medstat Inc, Ann Arbor, MI, USA) and All Patient Refined Diagnosis Related Groups (3M Health Information Systems, Wallingford, CT, USA) is warranted [36]. In addition, we currently are testing and validating a specific comorbidity-based measure for outcome prediction after orthopaedic surgery. The Elixhauser comorbidity measure outperformed the widely used Charlson measure in predicting inpatient mortality and morbidity after major orthopaedic surgery, and its more extensive use in claims-based studies should be considered. Future research assessing and comparing the performance of these measures in predicting long-term outcomes would be of value.


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List of ICD-9 codes included to identify adverse events

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