Economic Burden of Surgical Management of Prosthetic Joint Infections Following Hip and Knee Replacements in Alberta, Canada: An analysis and comparison of two major urban centers

Current Concepts on the Clinical and Economic Impact of Periprosthetic Joint Infections

Total joint arthroplasty (TJA) is a common procedure performed throughout the entire world in hopes of alleviating debilitating hip or knee pain. The projected number of TJAs performed in the United States alone is projected to exceed 1.9 million by 2030 and 5 million by 2040. With the significant increase in TJA performed, more periprosthetic joint infections (PJIs) are likely to be encountered. PJIs are a devastating complication of TJA. The economic and clinical burden must be understood and respected to minimize occurrence and allow optimal patient outcomes.

Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis

Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84-0.97), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60-611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.

Retrospective analysis of the management strategies for surgical site infections post total hip and knee arthroplasty from tertiary care centres in Edmonton, Alberta

BACKGROUND

Surgical site infection (SSI) following hip and knee arthroplasty poses serious health and economic burden.

AIM

To evaluate SSI management strategies and outcomes to help address this problem.

METHODS

We performed a retrospective cohort study of adults undergoing primary total hip or knee arthroplasty who were identified to have a complex SSI by infection prevention and control surveillance audit. Audits identified SSI within 90 days of arthroplasty. Patients at two tertiary referral centres in Edmonton, Alberta, Canada from 2012 to 2019 were included and SSI cases were followed for two years.

FINDINGS

We identified 240 SSIs and found that 202 (84%) were managed with debridement with antibiotics and implant retention (DAIR), of which 71% achieved cure. The use of any topical intra-operative antibiotic in DAIR was not associated with improved outcome (OR = 1.68, 95% CI 0.91 - 3.10, p-value = 0.097). DAIR performed at 31-90 days from arthroplasty had lower chance of cure compared to DAIR performed within 30 days, however this was not statistically significant (60 vs 73%, p-value = 0.123). Initial treatment failures requiring additional surgery had 51% cure. The majority (78%) of treatment failures initially managed with DAIR ultimately required two-stage revision.

CONCLUSIONS

We provide insight from a population-based perspective on the surgical management of SSIs after primary total hip and knee arthroplasty in a large cohort. Additionally, we follow SSIs that had initial management failure. This data can inform future studies such as the economic burden associated with these infections, and plan interventions to optimize SSI management.