Predicting Postoperative Complications and Mortality After Acetabular Surgery in the Elderly

A Comparison of Risk Stratification Models

Chang-Yeon Kim, MD; Nikunj N. Trivedi, MD; Lakshmanan Sivasundaram, MD; George Ochenjele, MD; Raymond W. Liu, MD; Heather Vallier, MD

Disclosures

Curr Orthop Pract. 2020;31(2):162-167. 

In This Article

Results

A total of 2,471 patients 65 yr of age or older who had undergone operative intervention for a primary diagnosis of acetabular fracture were included in our study. Our cohort's mean age was 76±7 yr, and male patients accounted for 61.6% (1436/2471) of the study sample (Table 2). The majority (77.9%; 1815/2471) of our patients were white, and 72.3% (1684/2471) of our patients identified Medicare as their primary insurance.

Comorbidities

The three most common Elixhauser comorbidities were hypertension (61.8, 1440/2330%), fluid and electrolyte disorders (29.0%, 676/2330), and diabetes without complications (20.7%, 508/2330); the three most common Charlson comorbidities were diabetes without complications (21.8%, 508/2330), chronic pulmonary disease (15.3%, 356/2330), and congestive heart failure (11.7%, 273/2330).

In our cohort, 79.9% (1862/2330) were discharged to a skilled nursing facility or rehabilitation center. Overall, 28.2% (657/2330) of patients sustained a postoperative complication. The most common complications were renal in nature (11.4%, 266/2330), followed by pulmonary (9.1%, 212/2330), thromboembolic (5.3%, 23/2330), and cardiac (4.1%, 96/2330). The mortality rate was 2.6% (61/2330).

The ECM was more accurate (higher C-statistic) than the CCI at predicting adverse outcomes. The model using demographic variables and the full set of ECM binary variables (base+ECM complete) provided better predictive discrimination, with an AUC of 0.813 (95% CI 0.763–0.863) for mortality, 0.677 (95% CI 0.655–0.698) for extended length of stay, and 0.672 (95% CI 0.646–0.699) for discharge to a post-acute care facility (Table 3; Figure 1). The model using demographic variables and the Charlson binary variables (base+CCI complete) provided had an AUC of 0.734 (95% CI 0.667–0.8) for mortality, 0.612 (95% CI 0.589–0.635) for extended length of stay, and 0.663 (95% CI 0.636–0.69) for discharge to a skilled nursing facility (SNF) or rehab. Results were similar for postoperative complications, where the model with demographic variables and the full set of binary ECM variables (base+ECM complete) provided better discrimination compared to the CCI model (Table 3).

Figure 1.

Receiver operating characteristic (ROC) curves for extended length of stay (LOS), discharge to a postacute care facility, and mortality.

Combining the models to form the CCS led to further improvement in predictive discrimination (Table 3). The model using demographic variables and the full set of combined binary variables (base+CCM complete) resulted in AUC of 0.829 (0.781–0.878) for mortality, 0.677 (95% CI 0.655–0.698) for extended length of stay, and 0.672 (95% CI 0.646–0.699) for discharge to a post-acute care facility (Table 3; Figure 1).

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