Primary Total Knee Arthroplasty Performed Using High-Viscosity Cement is Associated With Higher Odds of Revision for Aseptic Loosening.

BACKGROUND: Aseptic loosening (AL) is the most common reason for revision total knee arthroplasty (TKA). An association between high-viscosity cement (HVC) and AL has been suggested by small, uncontrolled, case series. This study sought to determine whether HVC use during primary TKA is independently associated with AL requiring revision. METHODS: We retrospectively analyzed a prospectively collected institutional knee registry to identify all primary TKAs from January 2007 to December 2016. Patients with less than 2 years of follow-up were excluded. Cement type was divided into 2 groups: HVC and low-viscosity cement. Potential confounders including age, body mass index, preoperative diagnosis, antibiotics in the cement, and implant type were recorded. Multivariable logistic regression analysis was used to determine whether HVC is independently associated with revision for AL. RESULTS: In total, 10,014 patients were included. Revision for AL was significantly higher in the HVC cohort (91/4790; 1.9%) vs the low-viscosity cement cohort (48/5224; 0.92%) (P < .001). Logistic regression demonstrated HVC to be independently associated with higher odds of revision for AL (odds ratio 2.26, 95% confidence interval 1.58-3.22, P < .001). Younger age was also associated with higher odds of revision for AL (odds ratio 0.96, 95% confidence interval 0.94-0.98, P < .001). Body mass index, gender, laterality, preoperative diagnosis, and antibiotics in the cement were not associated with revision for AL. Implant manufacturer, implant design, and cement brand all impacted the odds of undergoing revision for AL. CONCLUSION: Although HVC is an attractive option for use in primary TKA, this appropriately controlled study demonstrates higher odds of revision for AL when using HVC with multiple different implant types.

Predicting implant size in total knee arthroplasty using demographic variables.

AIMS: The purpose of this investigation was to determine the relationship between height, weight, and sex with implant size in total knee arthroplasty (TKA) using a multivariate linear regression model and a Bayesian model. METHODS: A retrospective review of an institutional registry was performed of primary TKAs performed between January 2005 and December 2016. Patient demographics including patient age, sex, height, weight, and body mass index (BMI) were obtained from registry and medical record review. In total, 8,100 primary TKAs were included. The mean age was 67.3 years (SD 9.5) with a mean BMI of 30.4 kg/m2 (SD 6.3). The TKAs were randomly split into a training cohort (n = 4,022) and a testing cohort (n = 4,078). A multivariate linear regression model was created on the training cohort and then applied to the testing cohort . A Bayesian model was created based on the frequencies of implant sizes in the training cohort. The model was then applied to the testing cohort to determine the accuracy of the model at 1%, 5%, and 10% tolerance of inaccuracy. RESULTS: Height had a relatively strong correlation with implant size (femoral component anteroposterior (AP) Pearson correlation coefficient (ρ) = 0.73, p < 0.001; tibial component mediolateral (ML) ρ = 0.77, p < 0.001). Weight had a moderately strong correlation with implant size, (femoral component AP ρ = 0.46, p < 0.001; tibial ML ρ = 0.48, p < 0.001). There was a significant linear correlation with height, weight, and sex with implant size (femoral component R2 = 0.607, p < 0.001; tibial R2 = 0.695, p < 0.001). The Bayesian model showed high accuracy in predicting the range of required implant sizes (94.4% for the femur and 96.6% for the tibia) accepting a 5% risk of inaccuracy. CONCLUSION: Implant size was correlated with basic demographic variables including height, weight, and sex. The linear regression and Bayesian models accurately predicted required implant sizes across multiple manufacturers based on height, weight, and sex alone. These types of predictive models may help improve operating room and implant supply chain efficiency. Level of Evidence: Level IV Cite this article: Bone Joint J 2020;102-B(6 Supple A):85-90.