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). 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.Aims
Methods
The aim of this study was to describe a quantitative 3D CT method to measure rotator cuff muscle volume, atrophy, and balance in healthy controls and in three pathological shoulder cohorts. In all, 102 CT scans were included in the analysis: 46 healthy, 21 cuff tear arthropathy (CTA), 18 irreparable rotator cuff tear (IRCT), and 17 primary osteoarthritis (OA). The four rotator cuff muscles were manually segmented and their volume, including intramuscular fat, was calculated. The normalized volume (NV) of each muscle was calculated by dividing muscle volume to the patient’s scapular bone volume. Muscle volume and percentage of muscle atrophy were compared between muscles and between cohorts.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 primary aim of this paper was to outline the processes involved in building the Partners Arthroplasty Registry (PAR), established in April 2016 to capture baseline and outcome data for patients undergoing arthroplasty in a regional healthcare system. A secondary aim was to determine the quality of PAR’s data. A tertiary aim was to report preliminary findings from the registry and contributions to quality improvement initiatives and research up to March 2019. Structured Query Language was used to obtain data relating to patients who underwent total hip or knee arthroplasty (THA and TKA) from the hospital network’s electronic medical record (EMR) system to be included in the PAR. Data were stored in a secure database and visualized in dashboards. Quality assurance of PAR data was performed by review of the medical records. Capture rate was determined by comparing two months of PAR data with operating room schedules. Linear and binary logistic regression models were constructed to determine if length of stay (LOS), discharge to a care home, and readmission rates improved between 2016 and 2019.Aims
Methods
Custom flange acetabular components (CFACs) are a patient-specific option for addressing large acetabular defects at revision total hip arthroplasty (THA), but patient and implant characteristics that affect survivorship remain unknown. This study aimed to identify patient and design factors related to survivorship. A retrospective review of 91 patients who underwent revision THA using 96 CFACs was undertaken, comparing features between radiologically failed and successful cases. Patient characteristics (demographic, clinical, and radiological) and implant features (design characteristics and intraoperative features) were collected. There were 74 women and 22 men; their mean age was 62 years (31 to 85). The mean follow-up was 24.9 months (Aims
Patients and Methods
