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. The aim of this study was to screen the entire genome for genetic markers associated with risk for anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) injury. Methods. Genome-wide association (GWA) analyses were performed using data from the Kaiser Permanente Research Board (KPRB) and the UK Biobank. ACL and PCL injury cases were identified based on electronic health records from KPRB and the UK Biobank. GWA analyses from both cohorts were tested for ACL and PCL injury using a logistic regression model adjusting for sex, height, weight, age at enrolment, and race/ethnicity using allele counts for single nucleotide polymorphisms (SNPs). The data from the two GWA studies were combined in a meta-analysis. Candidate genes previously reported to show an association with ACL injury in athletes were also tested for association from the meta-analysis data from the KPRB and the UK Biobank GWA studies. Results. There was a total of 2,214 cases of ACL and PCL injury and 519,869 controls within the two cohorts, with three loci demonstrating a genome-wide significant association in the meta-analysis: INHBA, AEBP2, and LOC101927869. Of the eight candidate genes previously studied in the literature, six were present in the current dataset, and only COL3A1 (rs1800255) showed a significant association (p = 0.006). Conclusion. Genetic markers in three novel loci in this study and one previously-studied candidate gene were identified as potential risk factors for ACL and PCL injury and deserve further validation and investigation of molecular mechanisms. Cite this article: Bone Jt Open 2021;2(6):414–421

Aims

Revision total knee arthroplasty (rTKA) is a technically challenging and costly procedure. It is well-documented that primary TKA (pTKA) have better survivorship than rTKA; however, we were unable to identify any studies explicitly investigating previous rTKA as a risk factor for failure following rTKA. The purpose of this study is to compare the outcomes following rTKA between patients undergoing index rTKA and those who had been previously revised.

Aims

The diagnosis of periprosthetic joint infection (PJI) can be challenging as the symptoms are similar to other conditions, and the markers used for diagnosis have limited sensitivity and specificity. Recent research has suggested using blood cell ratios, such as platelet-to-volume ratio (PVR) and platelet-to-lymphocyte ratio (PLR), to improve diagnostic accuracy. The aim of the study was to further validate the effectiveness of PVR and PLR in diagnosing PJI.

Objectives

A patient-centred approach, usually achieved through shared decision making, has the potential to help improve decision making around knee arthroplasty surgery. However, such an approach requires an understanding of the factors involved in patient decision making. This review’s objective is to systematically examine the qualitative literature surrounding patients’ decision making in knee arthroplasty.