What Associations Exist Between Comorbidity Indices and Postoperative Adverse Events After Total Shoulder Arthroplasty?

Background Comorbidity indices like the modified Charlson Comorbidity Index (mCCI) and the modified Frailty Index (mFI) are commonly reported in large database outcomes research. It is unclear if they provide greater association and discriminative ability for postoperative adverse events after total shoulder arthroplasty (TSA) than simple variables.

Questions/purposes Using a large research database to examine postoperative adverse events after anatomic and reverse TSA, we asked: (1) Which demographic/anthropometric variable among age, sex, and body mass index (BMI) has the best discriminative ability as measured by receiver operating characteristics (ROC)? (2) Which comorbidity index, among the American Society of Anesthesiologists (ASA) classification, the mCCI, or the mFI, has the best ROC? (3) Does a combination of a demographic/anthropometric variable and a comorbidity index provide better ROC than either variable alone?

Methods Patients who underwent TSA from 2005 to 2015 were identified from the National Surgical Quality Improvement Program (NSQIP). This multicenter database with representative samples from more than 600 hospitals in the United States was chosen for its prospectively collected data and documented superiority over administrative databases. Of an initial 10,597 cases identified, 70 were excluded due to missing age, sex, height, weight, or being younger than 18 years of age, leaving a total of 10,527 patients in the study. Demographics, medical comorbidities, and ASA scores were collected, while BMI, mCCI and mFI were calculated for each patient. Though all required data variables were found in the NSQIP, the completeness of data elements was not determined in this study, and missing data were treated as being the null condition. Thirty-day outcomes included postoperative severe adverse events, any adverse events, extended length of stay (LOS, defined as > 3 days), and discharge to a higher level of care. ROC analysis was performed for each variable and outcome, by plotting its sensitivity against one minus the specificity. The area under the curve (AUC) was used as a measure of model discriminative ability, ranging from 0 to 1, where 1 represents a perfectly accurate test, and 0.5 indicates a test that is no better than chance.

Results Among demographic/anthropometric variables, age had a higher AUC (0.587–0.727) than sex (0.520–0.628) and BMI (0.492–0.546) for all study outcomes (all p < 0.050), while ASA (0.580–0.630) and mFI (0.568–0.622) had higher AUCs than mCCI (0.532–0.570) among comorbidity indices (all p < 0.050). A combination of age and ASA had higher AUCs (0.608–0.752) than age or ASA alone for any adverse event, extended LOS, and discharge to higher level of care (all p < 0.05). Notably, for nearly all variables and outcomes, the AUCs showed fair or moderate discriminative ability at best.

Conclusion Despite the use of existing comorbidity indices adapted to large databases such as the NSQIP, they provide no greater association with adverse events after TSA than simple variables such as age and ASA status, which have only fair associations themselves. Based on database-specific coding patterns, the development of database- or NSQIP-specific indices may improve their ability to provide preoperative risk stratification.

Level of Evidence Level III, diagnostic study.