DOES ANTIBIOTIC-LOADED CEMENT REDUCE THE RISK OF PERIPROSTHETIC INFECTION IN PRIMARY TOTAL KNEE ARTHROPLASTY? ANALYSIS OF THE CATALAN ARTHROPLASTY REGISTRY

Aim There is controversy regarding the use of Antibiotic-loaded cement (ALBC) as compared to non-antibiotic-loaded cement (NALBC) to reduce the overall infection rate without affecting implant survival or adding additional risks on fixation for primary total knee arthroplasty (TKA). Method To conduct the analysis, we utilized the Catalan Arthroplasty Registry (RACat) for the TKAs implanted between 2005 and 2017. The primary variable recorded was the use of cement with or without antibiotics. Other recorded variables included were age, sex, diabetes mellitus, obesity, Charlson index and type of hospital. We analyzed the effect of ALBC vs. NALBC in reducing the risk of prosthetic infection at 3, 6, 12, and 24 months as well as prosthetic survival due to mechanical causes at 1, 5, and 10 years. Univariate and multivariable analyses of risk factors were conducted. Thereby, an interactive predictive model that determines the risk of prosthetic infection based on each patient's characteristics was created. Results A total of 28,287 TKAs from the RACat were analyzed. In that total, there were 19,788 NALBC and 8,499 ALBC. The infection rates for TKAs with NALBC vs. those with ALBC at 3, 6, 12, and 24 months were respectively: 1.69% vs. 1.39% (p=0.132); 1.81% vs. 1.56% (p=0.147); 2.14% vs. 1.73% (p=0.030); 2.51% vs. 1.86% (p=0.001). A statistically significant reduction in periprosthetic infection rate was observed in the ALBC group at 12 and 24 months. No differences were observed between the two groups in terms of prosthetic mechanical survival. Being younger, male and having had previous knee surgery or having a high comorbidity index all led to a higher risk of prosthetic revision due to infection. Conclusions The use of ALBC as a fixation method for TKA leads to a reduction in the risk of prosthetic infection without altering the mechanical survival of the implant. The creation of a predictive model helps determine the individualized risk of prosthetic infection based on the patient's characteristics.