Do Claims-based Comorbidities Adequately Capture Case Mix for Surgical Site Infections?

Abstract

Background There is increasing interest in using administrative claims data for surveillance of surgical site infections in THAs and TKAs, but the performance of claims-based models for case-mix adjustment has not been well studied. Performance of claims-based models can be improved with the addition of clinical risk factors for surgical site infections. Questions/purposes We assessed (1) discrimination and calibration of claims-based risk-adjustment models for surgical site infections; and (2) the incremental value of adding clinical risk factors to claims-based risk-adjustment models for surgical site infections. Patients and Methods Our study included all THAs and TKAs performed at a large tertiary care hospital from January 1, 2002 to December 31, 2009 (total n = 20,171 procedures). Revision procedures for infections were excluded. Comorbidity data were ascertained through administrative records and classified by the Charlson comorbidity index. Clinical details were obtained from the institutional joint registry and patients' electronic health records. Cox proportional hazards regression models were used to estimate the 1-year risk of surgical site infections with a robust sandwich covariance estimator to account for within-subject correlation of individuals with multiple surgeries. The performance of claims-based risk models with and without the inclusion of four clinical risk factors (morbid obesity, prior nonarthroplasties on the same joint, American Society of Anesthesiologists score, operative time) was assessed using measures of discrimination (C statistic, Somers' D xy rank correlation, and the Nagelkerke R 2 index). Furthermore, calibrations of claims-based risk models with and without clinical factors were assessed graphically by plotting the smoothed trends between model predictions and empirical rates from Kaplan-Meier. Results Discrimination of the claims-based risk models was moderate for the THA (C statistic = 0.662, D xy = 0.325, R 2 = 0.028) and TKA (C statistic = 0.621, D xy = 0.241, R 2 = 0.017) cohorts. Inclusion of four clinical risk factors improved discrimination in both cohorts with significant improvement in the C statistic in the THA cohort (C statistic = 0.043; 95% CI, 0.012-0.074) and in the TKA cohort (C statistic = 0.027; 95% CI, 0.007-0.047). Visual inspection suggested that calibration of the claims-based risk models was adequate and comparable to that of models which included the four additional clinical factors. Conclusions Claims-based risk-adjustment models for surgical site infections in THA and TKA appear to be adequately calibrated but lack predictive discrimination, particularly with TKAs. The addition of clinical risk factors improves the discriminative ability of the models to a moderate degree; however, addition of clinical factors did not change calibrations, as the models showed reasonable

Systematic review of risk prediction scores for surgical site infection or periprosthetic joint infection following joint arthroplasty

“…A total of 16 risk scores were described in the nine eligible studies. Five of these scores had separate models for hip and knee replacement patients [18, 22, 23]. Four studies described the development of two or more risk scores [18, 19, 22, 23].…”

“…Lewallen et al externally validated the HPRO and KPRO risk scores and reported that addition of information on morbid obesity and diabetes mellitus to each score modestly improved discrimination [21]. On addition of four clinical risk factors (morbid obesity, prior non-arthroplasties on the same joint, ASA score and operative time) to their claims-based risk models for THA and TKA, Maradit Kremers et al reported improved performance (by C-statistics) for both models, though the THA model showed better performance than the TKA model [23]. There was however no noticeable improvement in calibration for both models.…”

“…Five of these scores had separate models for hip and knee replacement patients [18, 22, 23]. Four studies described the development of two or more risk scores [18, 19, 22, 23]. All 16 risk scores were derivations of risk models on a base population and two of them were also externally validated on new populations [21].…”

Medicare’s New Bundled Payment For Joint Replacement May Penalize Hospitals That Treat Medically Complex Patients

“…For this purpose, clinical registry data may be an option if it can be linked to Medicare claims. 15–17 Clinical registry data often have robust risk adjustment variables that are not available from administrative claims. The trade-off is, however, that these data are labor-intensive and expensive to obtain.…”

Validation of Using Claims Data to Measure Safety of Lumbar Fusion Surgery

“…Claims data has been used to report trends in procedure rates, costs, and outcomes for spinal surgery in a more efficient manner than using manual chart review 1,2,3,4 . Several studies have shown a claims-based algorithm to reliably classify spine surgery indication and type of procedure 5,6 .…”