A Multisite Assessment of Inpatient Safety Event Rates During the Coronavirus Disease 2019 Pandemic.

To date, there has been a notable lack of peer-reviewed or publicly available data documenting rates of hospital quality outcomes and patient safety events during the coronavirus disease 2019 pandemic era. The dearth of evidence is perhaps related to the US health care system triaging resources toward patient care and away from reporting and research and also reflects that data used in publicly reported hospital quality rankings and ratings typically lag 2-5 years. At our institution, a learning health system assessment is underway to evaluate how patient safety was affected by the pandemic. Here we share and discuss early findings, noting the limitations of self-reported safety event reporting, and suggest the need for further widespread investigations at other US hospitals. During the 2-year study period from January 1, 2020, through December 31, 2021 across 3 large US academic medical centers at our institution, we documented an overall rate of 25.8 safety events per 1000 inpatient days. The rate of events meeting "harm" criteria was 12.4 per 1000 inpatient days, the rate of nonharm events was 11.1 per 1000 inpatient days, and the fall rate was 2.3 per 1000 inpatient days. This descriptive exploratory analysis suggests that patient safety event rates at our institution did not increase over the course of the pandemic. However, increasing health care worker absences were nonlinearly and strongly associated with patient safety event rates, which raises questions regarding the mechanisms by which patient safety event rates may be affected by staff absences during pandemic peaks.

Cost and Patient Outcomes Associated With Bilateral Total Knee Arthroplasty Performed by 2-Surgeon Teams vs a Single Surgeon.

BACKGROUND: Bilateral total knee arthroplasty (TKA) can be performed under a single-anesthetic (SA) or staged under a two-anesthetic (TA) technique. Recently, our institution began piloting a 2-surgeon team SA method for bilateral TKA. The purpose of this study was to compare the inpatient costs and clinical outcomes in the first 90 days after surgery between the team SA, single-surgeon SA, and single-surgeon TA approaches for bilateral TKA. METHODS: All primary TKAs performed from 2007 to 2017 by the 2 participating surgeons for each of the 3 groups of interest were identified: team SA (N = 42 patients; 84 knees), single-surgeon SA (N = 146 patients; 292 knees), single-surgeon TA (N = 242 patients; 484 knees). No patients were lost to follow-up. RESULTS: Median hospital cost (per TKA) for the episode(s) of care was as follows: team SA $20,962, single-surgeon SA $22,057, single-surgeon TA $31,145 (P < .001 overall; P = .0905 team SA vs single-surgeon SA). Rate of 90-day complications was 2.4% for team SA, 11.0% for single-surgeon SA, and 8.3% for single-surgeon TA (P = .2090). Discharge to skilled nursing facilities or rehab was as follows: team SA 31%, single-surgeon SA 53%, and single-surgeon TA after the second operation 34% (P < .001). CONCLUSION: This pilot project suggests that team SA bilateral TKA is a potentially cost-effective option with fewer complications compared to single-surgeon SA bilateral TKA. The less frequent disposition to skilled nursing facilities in the team SA group in conjunction with more efficient operating room utilization may further enhance the financial benefits.

Do claims-based comorbidities adequately capture case mix for surgical site infections?

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 degrees of calibration. When used in the clinical setting, the predictive performance of claims-based risk-adjustment models may be improved further with inclusion of additional clinical data elements.