||Boyle KK, Kapadia M, Chiu YF, Khilnani T, Miller AO, Henry MW, Lyman S, Carli AV., The James A. Rand Young Investigator's Award: Are Intraoperative Cultures Necessary If the Aspiration Culture Is Positive? A Concordance Study in Periprosthetic Joint Infection., Journal of Arthroplasty, 10.1016/j.arth.2021.01.073., 2021.07, Background: The concordance between preoperative synovial fluid culture and multiple intraoperative tissue cultures for identifying pathogenic microorganisms in periprosthetic joint infection (PJI) remains unknown. Our aim is to determine the diagnostic performance of synovial fluid culture for early organism identification.
Methods: A total of 363 patients who met Musculoskeletal Infection Society criteria for PJI following primary total joint arthroplasty were identified from a retrospective joint infection database. Inclusion criteria required a positive preoperative intra-articular synovial fluid sample within 90 days of intraoperative tissue culture(s) at revision surgery. Concordance was defined as matching organism(s) in aspirate and intraoperative specimens.
Results: Concordance was identified in 279 (76.8%) patients with similar rates among total hip arthroplasties (77.2%) and total knee arthroplasties (76.4%, P = .86). Culture discordance occurred in 84 (23.1%) patients; 37 (10.2%) had no intraoperative culture growth and 33 (90.1%) were polymicrobial. Monomicrobial Staphylococcal PJI cases had high sensitivity (0.96, 95% confidence interval [CI] 0.92-0.98) and specificity (0.85, 95% CI 0.80-0.90). Polymicrobial infections had the lowest sensitivity (0.06, 95% CI 0.01-0.19).
Conclusion: Aspiration culture has favorable sensitivity and specificity when compared to tissue culture for identifying the majority of PJI organisms. Clinicians can guide surgical treatment and postoperative antibiotics based on monomicrobial aspiration results, but they should strongly consider collecting multiple tissue cultures to maximize the chance of identifying an underlying polymicrobial PJI..
||Polascik BA, Hidaka C, Thompson MC, Tong-Ngork S, Wagner JL, Plummer O, Lyman S., Crosswalks Between Knee and Hip Arthroplasty Short Forms: HOOS/KOOS JR and Oxford, Journal of Bone and Joint Surgery, 10.2106/JBJS.19.00916., 2020.06, Background: The Oxford Knee Score (OKS); Oxford Hip Score (OHS); Knee injury and Osteoarthritis Outcome Score, Joint Replacement (KOOS JR); and Hip disability and Osteoarthritis Outcome Score, Joint Replacement (HOOS JR) are well-validated and widely used short-form patient-reported outcome measures (PROMs) for assessing outcomes after total knee arthroplasty (TKA) and total hip arthroplasty (THA). We are not aware of the existence of any crosswalks to convert scores between these PROMs. We aimed to develop and validate crosswalks that will permit the comparison of scores between studies using different PROMs and the pooling of results for meta-analyses.
Methods: We retrospectively analyzed scores from patients (486 in the knee cohort and 340 in the hip cohort) from the Syracuse Orthopedic Specialists Joint Registry who had completed the appropriate PROMs (OKS and KOOS JR in the knee cohort and OHS and HOOS JR in the hip cohort) as the standard of care before undergoing primary TKA or unicompartmental knee arthroplasty (UKA) between January 9, 2016, and June 19, 2017, or primary THA or hip resurfacing between November 29, 2010, and October 30, 2017, or when returning for postoperative care. Using the equipercentile equating method, we created 4 crosswalks: OKS to KOOS JR, KOOS JR to OKS, OHS to HOOS JR, and HOOS JR to OHS. To assess validity, Spearman coefficients were calculated using bootstrapping methods, and means for actual and crosswalk-derived scores were compared.
Results: There were minimal differences between the means of the known and crosswalk-derived scores. As calculated with the use of bootstrapping methods, Spearman coefficients between the actual and derived scores were strong and positive for both knee arthroplasty crosswalks (0.888 to 0.889; 95% confidence interval [CI], 0.887 to 0.891) and hip arthroplasty crosswalks (0.916 to 0.918; 95% CI, 0.914 to 0.919).
Conclusions: We successfully created 4 crosswalks that allow conversion of Oxford scores to KOOS and HOOS JR scores and vice versa. These crosswalks will allow harmonization of PROMs assessment regardless of which of the short forms are used, which may facilitate multicenter collaboration or allow sites to switch PROMs without loss of historic comparison data..
||Blevins JL, Rao V, Chiu YF, Lyman S, Westrich GH., Predicting implant size in total knee arthroplasty using demographic variables., Bone and Joint Journal, 10.1302/0301-620X.102B6.BJJ-2019-1620.R1., 2020.06, 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..