Neuraxial and general anesthesia for outpatient total joint arthroplasty result in similarly low rates of major perioperative complications: a multicentered cohort study

BackgroundNeuraxial anesthesia when compared with general anesthesia has shown to improve outcomes following lower extremity total joint arthroplasty. It is unclear whether these benefits are present in outpatient surgery given the selection of healthier patients.ObjectiveTo compare the effects of neuraxial versus general anesthesia on outcomes following ambulatory hip and knee arthroplasty.MethodsMulticentered retrospective cohort study in ambulatory hip or knee arthroplasty patients between January 2017 and December 2019. Primary endpoint examined 30-day major postoperative complications (mortality, myocardial infarction, deep venous thromboembolism, pulmonary embolism, stroke, and acute renal failure).ResultsOf 11 523 eligible patients identified, 10 003 received neuraxial anesthesia, while 1520 received general anesthesia. 30-day major complications did not differ between neuraxial anesthesia and general anesthesia groups (1.8% vs 2.3%; aOR=0.85, CI: 0.56 to 1.27, p=0.39). There was no difference in 30-day minor complications (surgical site infection, pneumonia, urinary tract infection; 3.3% vs 4.1%; aOR=0.83, CI: 0.62 to 1.14, p=0.23). The neuraxial group demonstrated reduced pain and analgesia requirements and had less postoperative nausea and vomiting (PONV). Median recovery room length of stay was shorter by 52 min in the general anesthesia group, but these patients were more likely to fail same day discharge (33% vs 23.4%; p<0.01).ConclusionAnesthesia type was not associated with an increased risk for complications. However, neuraxial anesthesia improved outcomes that predict readiness for discharge: patients had less pain, required less opioids, and had a lower incidence of PONV, thus improving the rate of same day discharge.Trial registration numberNCT04203732.

Enhanced recovery after minimally invasive gynecologic oncology surgery to improve same day discharge: a quality improvement project

ObjectivesSame day discharge after minimally invasive hysterectomy has been shown to be safe and feasible. We designed and implemented a quality improvement perioperative program based on early recovery after surgery principles to improve the rate of same day discharge from 30% to 75% after minimally invasive gynecologic oncology surgery over a 12 month period.MethodsWe enrolled 102 consecutive patients undergoing minimally invasive hysterectomy at a single cancer center during a 12 month period. A pre-intervention cohort of 100 consecutive patients was identified for comparison of clinicodemographic variables and perioperative outcomes. A multidisciplinary team developed a comprehensive perioperative care program and followed quality improvement methodology. Patients were followed up for 30 days after discharge. A statistical process chart was used to monitor the effects of our interventions, and a multivariate analysis was conducted to determine factors associated with same day discharge.ResultsSame day discharge rate increased from 29% to 75% after implementation (p<0.001). The post-intervention cohort was significantly younger (59 vs 62 years; p=0.038) and had shorter operative times (180 vs 211 min; p<0.001) but the two groups were similar in body mass index, comorbidity, stage, and intraoperative complications. There was no difference in 30 day perioperative complications, readmissions, reoperations, emergency department visits, or mortality. Overnight admissions were secondary to nausea and vomiting (16%), complications of pre-existing comorbidities (12%), and urinary retention (8%). On multivariate analysis, longer surgery, timing of surgery, and narcotic use on the ward were significantly associated with overnight admission. Overall, 89% of patients rated their experience as ‘very good’ or ‘excellent’, and 87% felt that their length of stay was adequate.ConclusionsFollowing implementation of a perioperative quality improvement program targeted towards minimally invasive gynecologic oncology surgery, our intervention significantly improved same day discharge rates while maintaining a low 30 day perioperative complication rate and excellent patient experience.

COVID-19 as a Catalyst for Same-Day Discharge Total Shoulder Arthroplasty

The COVID-19 pandemic caused major disruptions to the healthcare system, but its impact on the transition to same-day discharge shoulder arthroplasty remains unexplored. This study assessed the effect of COVID-19 on length of stay (LOS), same-day discharge rates, and other markers of resource use after elective total shoulder arthroplasty. A total of 508 consecutive patients undergoing elective primary total shoulder arthroplasty between 2019 and 2021 were identified and divided into 2 cohorts: “pre-COVID” (March 2019–March 2020; n = 263) and “post-COVID” (May 2020–March 2021; n = 245). No elective shoulder arthroplasties were performed at our practice between 18 March and 11 May 2020. Outcome measures included LOS, same-day discharge, discharge location, and 90-day emergency department (ED) visits, readmissions and reoperations. There were no significant differences in baseline preoperative patient characteristics. Shoulder arthroplasty performed post-COVID was associated with a shorter LOS (12 vs. 16 h, p = 0.017) and a higher rate of same-day discharge (87.3 vs. 79.1%, p = 0.013). The rate of discharge to skilled nursing facilities was similarly low between the groups (1.9 vs. 2.0%, p = 0.915). There was a significant reduction in the rate of 90-day ED visits post-COVID (7.4 vs. 13.3%, p = 0.029), while there were no differences in 90-day reoperation (2.0 vs. 1.5%, p = 0.745) or readmission rates (1.2 vs. 1.9%, p = 0.724). The COVID-19 pandemic seems to have accelerated the shift towards shorter stays and more same-day discharge shoulder arthroplasties, while reducing unexpected acute health needs (e.g., ED visits) without adversely affecting readmission and reoperation rates.

The Relative Contribution of Medical Comorbidities in the OARA Score: A Machine Learning Analysis

Background/Objective: The Outpatient Arthroplasty Risk Assessment (OARA) score has been used successfully to identify patients who can safely undergo outpatient primary total joint arthroplasty (TJA) based on medical risk stratification. The targeted score (0 to 79) was conservatively established to ensure patient safety. However, the number of points associated with each of the 52 comorbidities in the OARA score were assigned based on physician experience with early discharge. This study applied machine learning (ML) to empirically identify the relative contribution/importance of each medical comorbidity to safe same-day discharge (SDD). Methods: 3,047 patients who underwent primary unilateral TJA by a single surgeon at a single institution were included in the analysis; 573 were SDDs. Before ML analysis, associations among binary (yes/no) comorbidities were examined using Cramér's V. A CART decision tree model using Gini method was used to develop a model for SDD (yes/no) based on the presence or absence of the comorbidities. Results: To produce interpretable results with acceptable face validity the 52 comorbidities were grouped in 19 common medical categories (heart disease, liver disease, etc.). Although the resulting model was less than perfectly predictive (AROC = 0.652, 95% CI 0.629–0.675), it resulted in an interpretable classification tree identifying heart disease, kidney disease, immunosuppression, chronic sedative use, pulmonary disease, thrombophilia, anemia, and history of stroke, in order, as the most important predictors of SDD. Conclusion: Model limitations expressed as AROC were not unexpected because the relative contribution (expressed as points) of comorbidities to the OARA score are based on physician decision-making, not empirical identification of the importance of each medical condition to safe SDD. Study results moved the goal of empirical classification forward but the low prevalence of many of the comorbidities limited variability and hence model performance and accuracy. Future work with a larger sample is being planned.