Aims. This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Methods. Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay. Results. The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis. Conclusion. Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358–1366

Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407

Aims. Intra-articular administration of antibiotics during primary total knee arthroplasty (TKA) may represent a safe, cost-effective strategy to reduce the risk of acute periprosthetic joint infection (PJI). Vancomycin with an aminoglycoside provides antimicrobial cover for most organisms isolated from acute PJI after TKA. However, the intra-articular doses required to achieve sustained therapeutic intra-articular levels while remaining below toxic serum levels is unknown. The purpose of this study is to determine the intra-articular and serum levels of vancomycin and tobramycin over the first 24 hours postoperatively after intra-articular administration in primary cementless TKA. Methods. A prospective cohort study was performed. Patients were excluded if they had poor renal function, known allergic reaction to vancomycin or tobramycin, received intravenous vancomycin, or were scheduled for same-day discharge. All patients received 600 mg tobramycin and 1 g of vancomycin powder suspended in 25 cc of normal saline and injected into the joint after closure of the arthrotomy. Serum from peripheral venous blood and drain fluid samples were collected at one, four, and 24 hours postoperatively. All concentrations are reported in µg per ml. Results. A total of 22 patients were included in final analysis. At one, four, and 24 hours postoperatively, mean (95% confidence interval (CI)) serum concentrations were 2.4 (0.7 to 4.1), 5.0 (3.1 to 6.9), and 4.8 (2.8 to 6.9) for vancomycin and 4.9 (3.4 to 6.3), 7.0 (5.8 to 8.2), and 1.3 (0.8 to 1.8) for tobramycin; intra-articular concentrations were 1,900.6 (1,492.5 to 2,308.8), 717.9 (485.5 to 950.3), and 162.2 (20.5 to 304.0) for vancomycin and 2,105.3 (1,389.9 to 2,820.6), 403.2 (266.6 to 539.7), and 98.8 (0 to 206.5) for tobramycin. Conclusion. Intra-articular administration of 1 g of vancomycin and 600 mg of tobramycin as a solution after closure of the arthrotomy in primary cementless TKA achieves therapeutic intra-articular concentrations over the first 24 hours postoperatively and does not reach sustained toxic levels in peripheral blood. Cite this article: Bone Joint J 2021;103-B(11):1702–1708

Aims. The aim of this prospective multicentre study was to describe trends in length of stay and early complications and readmissions following unicompartmental knee arthroplasty (UKA) performed at eight different centres in Denmark using a fast-track protocol and to compare the length of stay between centres with high and low utilization of UKA. Methods. We included data from eight dedicated fast-track centres, all reporting UKAs to the same database, between 2010 and 2018. Complete ( > 99%) data on length of stay, 90-day readmission, and mortality were obtained during the study period. Specific reasons for a length of stay of > two days, length of stay > four days, and 30- and 90-day readmission were recorded. The use of UKA in the different centres was dichotomized into ≥ 20% versus < 20% of arthroplasties which were undertaken being UKAs, and ≥ 52 UKAs versus < 52 UKAs being undertaken annually. Results. A total of 3,927 procedures were included. Length of stay (mean 1.1 days (SD 1.1), median 1 (IQR 0 to 1)) was unchanged during the study period. The proportion of procedures with a length of stay > two days was also largely unchanged during this time. The percentage of patients discharged on the day of surgery varied greatly between centres (0% to 50% (0 to 481)), with centres with high UKA utilization (both usage and volume) having a larger proportion of same-day discharges. The 30- and 90-day readmissions were 166 (4.2%) and 272 (6.9%), respectively; the 90-day mortality was 0.08% (n = 3). Conclusion. Our findings suggest general underutilization of the potential for quicker recovery following UKA in a fast-track setup. Cite this article: Bone Joint J 2020;102-B(9):1167–1175

Aims

This study aims to determine difference in annual rate of early-onset (≤ 90 days) deep surgical site infection (SSI) following primary total knee arthroplasty (TKA) for osteoarthritis, and to identify risk factors that may be associated with infection.

Aims

The rate of day-case total knee arthroplasty (TKA) in the UK is currently approximately 0.5%. Reducing length of stay allows orthopaedic providers to improve efficiency, increase operative throughput, and tackle the rising demand for joint arthroplasty surgery and the COVID-19-related backlog. Here, we report safe delivery of day-case TKA in an NHS trust via inpatient wards with no additional resources.

Aims

To evaluate the influence of discharge timing on 30-day complications following total knee arthroplasty (TKA).