Patients demonstrate distinct trajectories of recovery after THA. The purpose of this study was to assess the impact of adjacent muscle quality on postoperative hip kinematics. We hypothesized that patients with better adjacent muscle quality (less fatty infiltration) would have greater early biomechanical improvement. Adults undergoing primary THA were recruited. Preoperative MRI was obtained and evaluated via Scoring Hip Osteoarthritis with MRI Scores (SHOMRI, Lee, 2015). Muscle quality was assessed by measuring fat fraction [FF] from water-fat sequences. Biomechanics were assessed preoperatively and six weeks postoperatively during a staggered stance sit-to-stand using the Kinematic Deviation Index (KDI, Halvorson, 2022). Spearman's rho was used to assess correlations between muscle quality and function. Ten adults (5M, 5F) were recruited (average age: 60.1, BMI: 23.79, SHOMRI: 40.6, KDI: 2.96). Nine underwent a direct anterior approach and one a posterior approach. Preoperatively, better biomechanical function was very strongly correlated with lower medius FF (rho=0.89), strongly correlated with lower FF in the minimus (rho=0.75) and tensor fascia lata (TFL) FF (rho=0.70), and weakly correlated with SHOMRI (rho=0.29). At six weeks, greater biomechanical improvement was strongly correlated with lower minimus FF (rho=0.63), moderately correlated with medius FF (rho=0.59), and weakly correlated with TFL FF (rho=0.26) and SHOMRI (rho=0.39). Lastly, medius FF was moderately correlated with SHOMRI (rho=0.42) with negligible correlations between SHOMRI and FF in the minimus and TFL. These findings suggest adjacent muscle quality may be related to postoperative function following THA, explaining some of the variability and supporting specialized muscle rehabilitation or regeneration therapy to improve outcomes.
While interdisciplinary protocols and expedited surgical treatment improve management of geriatric hip fractures, the impact of such interventions on patients undergoing specifically arthroplasty for femoral neck fracture (FNF) has not been well studied. The aim of this study is to evaluate the efficacy of an interdisciplinary hip fracture protocol for patients undergoing arthroplasty for acute FNF. In 2017, our tertiary care institution implemented a standardized interdisciplinary hip fracture protocol. We conducted a retrospective review of adult patients who underwent hemiarthroplasty (HA) or total hip arthroplasty (THA) for FNF from July 2012 – March 2020, and compared patient characteristics, hospitalization characteristics, and outcomes between those treated before and after protocol implementation.Introduction
Methods
The purpose of this multicenter, randomized clinical trial was to determine the optimal dosing regimen of tranexamic acid (TXA) to minimize perioperative blood loss for revision total hip arthroplasty (THA). Six centers prospectively randomized 155 revisions to one of four regimens: 1g of intravenous (IV) TXA prior to incision, a double dose regimen of 1g IV TXA prior to incision and 1g IV TXA during wound closure, a combination of 1g IV TXA prior to incision and 1g intraoperative topical TXA, or three doses of 1950mg oral TXA administered 2 hours preoperatively, 6 hours postoperatively, and on the morning of postoperative day one. Randomization was based upon revision subgroups to ensure equivalent group distribution, including: femur only, acetabulum only, both component, explant/spacer, and second stage reimplantation. Patients undergoing an isolated modular exchange were excluded. An Background
Methods
Implant loosening is a common cause of a poor outcome and pain after total knee arthroplasty (TKA). Despite the increase in use of expensive techniques like arthrography, the detection of prosthetic loosening is often unclear pre-operatively, leading to diagnostic uncertainty and extensive workup. The objective of this study was to evaluate the ability of a machine learning (ML) algorithm to diagnose prosthetic loosening from pre-operative radiographs, and to observe what model inputs improve the performance of the model. 754 patients underwent a first-time revision of a total joint at our institution from 2012–2018. Pre-operative X-Rays (XR) were collected for each patient. AP and lateral X-Rays, in addition to demographic and comorbidity information, were collected for each patient. Each patient was determined to have either loose or fixed prosthetics based on a manual abstraction of the written findings in their operative report, which is considered the gold standard of diagnosing prosthetic loosening. We trained a series of deep convolution neural network (CNN) models to predict if a prosthesis was found to be loose in the operating room from the pre-operative XR. Each XR was pre-processed to segment the bone, implant, and bone-implant interface. A series of CNN models were built using existing, proven CNN architectures and weights optimized to our dataset. We then integrated our best performing model with historical patient data to create a final model and determine the incremental accuracy provided by additional layers of clinical information fed into the model. The models were evaluated by its accuracy, sensitivity and specificity.Background
Methods
80% of health data is recorded as free text and not easily accessible for use in research and QI. Natural Language Processing (NLP) could be used as a method to abstract data easier than manual methods. Our objectives were to investigate whether NLP can be used to abstract structured clinical data from notes for total joint arthroplasty (TJA). Clinical and hospital notes were collected for every patient undergoing a primary TJA. Human annotators reviewed a random training sample(n=400) and test sample(n=600) of notes from 6 different surgeons and manually abstracted historical, physical exam, operative, and outcomes data to create a gold standard dataset. Historical data collected included pain information and the various treatments tried (medications, injections, physical therapy). Physical exam information collected included ROM and the presence of deformity. Operative information included the angle of tibial slope, angle of tibial and femoral cuts, and patellar tracking for TKAs and approach and repair of external rotators for THAs. In addition, information on implant brand/type/size, sutures, and drains were collected for all TJAs. Finally, the occurrence of complications was collected. We then trained and tested our NLP system to automatically collect the respective variables. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard.Background
Methods
