Implementing operative eligibility thresholds based on body mass index (BMI) alone risks restricting access to improved pain, function, and quality-of-life. The purpose of this study was to: 1) investigate the relationship between BMI and improvements in 1-year patient reported outcome measures (PROMs), and 2) determine how many patients would have been denied 1-year improvements with specific BMI cut-offs. Data were collected on a prospective cohort of 3,214 TKA patients from 2015–2018. Clinically meaningful 1-year improvements were defined as 15 points for Knee Injury and Osteoarthritis Outcome Scores (KOOS) pain and Physical Function Shortform (PS), and 14 points for Knee-Related Quality-of-Life (KRQOL). For specific BMI cut-offs, the positive predictive value for predicting a failure to improve and number of patients denied surgery to avoid one failed improvement was calculated.Introduction
Methods
The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.Aims
Methods
Antibiotic-impregnated bone cement (AIBC) has been used for decades to treat and prevent post-operative infections in joint arthroplasty. Local delivery of antibiotics may theoretically have a bactericidal effect, however evidence supporting this is controversial and literature suggests its prophylactic use in primary total knee arthroplasty (TKA) is seldom justified. With evolving standards of care, historical data is no longer relevant in addressing the efficacy of AIBC in the contemporary TKA. The purpose of this study was to evaluate outcomes following primary TKA using AIBC and regular non-AIBC by comparing rates of surgical site infection (SSI) and prosthetic joint infection (PJI). A retrospective review was conducted of all cemented primary TKA procedures from a large institutional database between January 1, 2015 and December 31st, 2016. This identified 6,073 cases, n=2,613 in which AIBC was used and n=3,460 cases using bone cement without antibiotics. Patients were stratified into low risk and high-risk groups based on age (>65 years), BMI (>40), and Charlson Comorbidity Index (CCI; >3). Medical records were reviewed for diagnoses of SSI (skin and superficial wound infections) and PJI (deep joint infections requiring surgery) over a 2-year postoperative period. Univariate analysis and multivariate regression models were used to ascertain the effects of cement type, patient factors (age, gender, BMI, CCI), operative time, and length of stay on infection rates. Additionally, mixed models (adjusted for gender, age, race, BMI, and CCI) were built to account for surgeon variability.Introduction
Methods
