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

In-hospital length of stay (LOS) and discharge dispositions following arthroplasty could act as surrogate measures for improvement in patient pathways, and have major cost saving implications for healthcare providers. With the ever-growing adoption of robotic technology in arthroplasty, it is imperative to evaluate its impact on LOS. The objectives of this study were to compare LOS and discharge dispositions following robotic arm-assisted total knee arthroplasty (RO TKA) and unicompartmental arthroplasty (RO UKA) versus conventional technique (CO TKA and UKA).

Aims

Despite recent literature questioning their use, vancomycin and clindamycin often substitute cefazolin as the preoperative antibiotic prophylaxis in primary total knee arthroplasty (TKA), especially in the setting of documented allergy to penicillin. Topical povidone-iodine lavage and vancomycin powder (VIP) are adjuncts that may further broaden antimicrobial coverage, and have shown some promise in recent investigations. The purpose of this study, therefore, is to compare the risk of acute periprosthetic joint infection (PJI) in primary TKA patients who received cefazolin and VIP to those who received a non-cephalosporin alternative and VIP.

We are entering a new era with governmental bodies taking an increasingly guiding role, gaining control of registries, demanding direct access with release of open public information for quality comparisons between hospitals. This review is written by physicians and scientists who have worked with the Swedish Knee Arthroplasty Register (SKAR) periodically since it began. It reviews the history of the register and describes the methods used and lessons learned.

Cite this article: Bone Joint Res 2014;3:217–22.