Cost of investigations during the acute hospital stay following total hip or knee arthroplasty, by complication status

Predictors of Higher Costs Following Reverse Total Shoulder Arthroplasty

Objectives

The rising popularity of reverse total shoulder arthroplasties (RTSA) demands attention to its growing costs on the healthcare system, especially with the implementation of bundled payments. Charges associated with patients' inpatient stays can be mitigated with a better understanding of the drivers of cost following RTSA. In this study, we evaluate potential pre-operative and post-operative factors associated with higher inpatient costs following RTSA.

Methods

We identified 59,925 patients who underwent RTSA using the National Inpatient Sample between 2016 and 2019. Total inpatient hospital charges were collected, and patients were divided into "normal cost" or "high cost" groups. The high cost group was defined as patients with total costs greater than the 75th percentile. Univariate and multivariate analyses were performed on pre-operative demographic and comorbidity variables as well as post-operative surgical and medical complications to predict factors associated with higher costs. T-tests and Chi-squared tests were performed, and odds ratios were calculated.

Results

The mean total charges were $141.213.93 in the high cost group and $59,181.94 in the normal cost group. Following multivariate analysis, non-white patients were associated with higher costs by 1.31-fold (P<0.001), but sex and age were not. Cirrhosis and non-elective admission had higher odds of higher costs by 1.56-fold (P<0.001) and 3.13-fold (P<0.001), respectively. Among surgical complications, there were higher odds of high costs for periprosthetic infection by 2.43-fold (P<0.001), periprosthetic mechanical complication by 1.28-fold (P<0.001), and periprosthetic fracture by 1.56-fold (P<0.001). Medical complications generally had higher odds of high costs than surgical complications, with deep vein thrombosis having nearly five times (P<0.001) and myocardial infarction almost four times (P<0.001) higher odds of high inpatient costs.

Conclusion

Post-operative medical complications were the most predictive factors of higher cost following RTSA. Pre-operative optimization to prevent infection and medical complications is imperative to mitigate the economic burden of RTSA's.

The reimbursement system can influence the treatment choice and favor joint replacement versus other less invasive solutions in patients affected by osteoarthritis

PURPOSE

The aim of this study was to assess how physicians perceive the role of the reimbursement system and its potential influence in affecting their treatment choice in the management of patients affected by osteoarthritis (OA).

METHODS

A survey was administered to 283 members of SIAGASCOT (Italian Society of Arthroscopy, Knee, Upper Limb, Sport, Cartilage and Orthopaedic Technologies), a National scientific orthopaedic society. The survey presented multiple choice questions on the access allowed by the current Diagnosis-Related Groups (DRG) system to all necessary options to treat patients affected by OA and on the influence toward prosthetic solutions versus other less invasive options.

RESULTS

Almost 70% of the participants consider that the current DRG system does not allow access to all necessary options to best treat patients affected by OA. More than half of the participants thought that the current DRG system favors the choice of prosthetic solutions (55%) and that it can contribute to the increase in prosthetic implantation at the expense of less invasive solutions (54%). The sub-analyses based on different age groups, professional roles, and places of work allowed to evaluate the response in each specific category, confirming the findings for all investigated aspects.

CONCLUSIONS

This survey documented that the majority of physicians consider that the reimbursement system can influence the treatment choice when managing OA patients. The current DRG system was perceived as unbalanced in favor of the choice of the prosthetic solution, which could contribute to the increase in prosthetic implantation at the expense of other less invasive options for OA management.

Frailty Among Total Hip and Knee Arthroplasty Recipients: Epidemiology and Propensity Score-weighted Analysis of Effect on In-hospital Postoperative Outcomes

INTRODUCTION

Total joint arthroplasty (TJA) is one of the most successful and frequently performed procedures in the United States. The number of these procedures is projected to continue growing rapidly in the coming years, and with it comes the demand for more sophisticated perioperative risk and complication assessment. This study examines the effect of frailty on postoperative inpatient complications and hospital resource utilization after TJA.

METHODS

Discharge data from the National Inpatient Sample were used to identify all patients aged 50 years or older who underwent TJA between 2006 and 2015. Nonelective admissions and hip fractures were excluded. Patients were stratified into two groups with and without concomitant ICD-9 diagnostic criteria that qualified them has having frailty. An analysis comparing the 2 groups' epidemiology, medical comorbidities, and propensity score-weighted postoperative clinical and economic outcomes was done.

RESULTS

A total of 7,854,890 TJAs were included in this analysis, with 136,516 meeting the criteria for frailty and 7,718,374 being nonfrail. Among these patients, the average age was 67.3 years and the female distribution was 61.1%. Frail patients were found to have markedly higher rates of all but two individual comorbidities constituting the Modified Elixhauser Profile compared with nonfrail patients. Compared with the control group, frail patients were found to have increased risk of any postoperative complication, central nervous system complications, hematoma/seroma, wound dehiscence, infection, and postoperative anemia. Frail patients also had longer length of stay, higher discharge to rehabilitation facilities, and higher hospital charges.

DISCUSSION

Patients with frailty undergoing TJA procedures are at a markedly higher risk for developing postoperative complications and worse hospital economic outcomes. As this patient population continues to increase, it is imperative for clinicians to use their risk factors in optimizing their perioperative care and support.

Predicting Postoperative Anemia and Blood Transfusion Following Total Knee Arthroplasty

BACKGROUND

While transfusion and clinically relevant anemia after elective primary total knee arthroplasty (TKA) are uncommon, there remains a question of who needs postoperative hemoglobin monitoring, especially in the setting of increasing incidence of outpatient TKA. The purpose of this study was to create predictive models for postoperative anemia and blood transfusion to guide clinical decision-making.

METHODS

The records of consecutive TKA patients were reviewed from February 2016 to December 2020 at a single institution. Two multivariable logistic regression models, for postoperative anemia (hemoglobin < 10 g/dL) and allogeneic blood transfusion included 8 variables: age, sex, body mass index, preoperative hemoglobin level, tranexamic acid total dose, American Society of Anesthesiologists level, operative time, and drain use. Model performance was assessed using accuracy, area under the curve (AUC), sensitivity, and specificity.

RESULTS

The records of 14,901 patients were included in this study. Patients had a mean (± standard deviation) age of 67.9 ± 9.2 years and mean body mass index of 31.3 ± 6.5 kg/m². The postoperative anemia model had an accuracy of 88% (95% confidence interval [CI], 87%-89%) and AUC of 0.88 (95% CI, 0.87-0.89). The blood transfusion model had an accuracy of 97% (95% CI, 96%-97%) and AUC of 0.90 (95% CI, 0.87-0.93).

CONCLUSION

The postoperative anemia and blood transfusion model accurately predicted each outcome. Patients with less than a 5% probability of postoperative anemia may not benefit from a complete blood count at postoperative day 1. Application of these criteria may save the healthcare system hundreds of millions of dollars.

LEVEL OF EVIDENCE

Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.