The current study aimed to compare robotic arm-assisted (RA-THA), computer-assisted (CA-THA), and manual (M-THA) total hip arthroplasty regarding in-hospital metrics including length of stay (LOS), discharge disposition, in-hospital complications, and cost of RA-THA versus M-THA and CA-THA versus M-THA, as well as trends in use and uptake over a ten-year period, and future projections of uptake and use of RA-THA and CA-THA. The National Inpatient Sample was queried for primary THAs (2008 to 2017) which were categorized into RA-THA, CA-THA, and M-THA. Past and projected use, demographic characteristics distribution, income, type of insurance, location, and healthcare setting were compared among the three cohorts. In-hospital complications, LOS, discharge disposition, and in-hospital costs were compared between propensity score-matched cohorts of M-THA versus RA-THA and M-THA versus CA-THA to adjust for baseline characteristics and comorbidities.Aims
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
The diagnosis of periprosthetic joint infection (PJI) is difficult and requires a battery of tests and clinical findings. The purpose of this review is to summarize all current evidence for common and new serum biomarkers utilized in the diagnosis of PJI. We searched two literature databases, using terms that encompass all hip and knee arthroplasty procedures, as well as PJI and statistical terms reflecting diagnostic parameters. The findings are summarized as a narrative review.Objectives
Methods
Dislocation remains a major concern after total hip replacement, and is often attributed to malposition of the components. The optimum position for placement of the components remains uncertain. We have attempted to identify a relatively safe zone in which movement of the hip will occur without impingement, even if one component is positioned incorrectly. A three-dimensional computer model was designed to simulate impingement and used to examine 125 combinations of positioning of the components in order to allow maximum movement without impingement. Increase in acetabular and/or femoral anteversion allowed greater internal rotation before impingement occurred, but decreases the amount of external rotation. A decrease in abduction of the acetabular components increased internal rotation while decreasing external rotation. Although some correction for malposition was allowable on the opposite side of the joint, extreme degrees could not be corrected because of bony impingement. We introduce the concept of combined component position, in which anteversion and abduction of the acetabular component, along with femoral anteversion, are all defined as critical elements for stability.
