It is important to analyze objectively the hammering sound in cup press-fit technique in total hip arthroplasty (THA) in order to better understand the change of the sound during impaction. We hypothesized that a specific characteristic would present in a hammering sound with successful fixation. We designed the study to quantitatively investigate the acoustic characteristics during cementless cup impaction in THA. In 52 THAs performed between November 2018 and April 2022, the acoustic parameters of the hammering sound of 224 impacts of successful press-fit fixation, and 55 impacts of unsuccessful press-fit fixation, were analyzed. The successful fixation was defined if the following two criteria were met: 1) intraoperatively, the stability of the cup was retained after manual application of the torque test; and 2) at one month postoperatively, the cup showed no translation on radiograph. Each hammering sound was converted to sound pressures in 24 frequency bands by fast Fourier transform analysis. Basic patient characteristics were assessed as potential contributors to the hammering sound.Aims
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The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset.Aims
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A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
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To map literature on prognostic factors related to outcomes of revision total knee arthroplasty (rTKA), to identify extensively studied factors and to guide future research into what domains need further exploration. We performed a systematic literature search in MEDLINE, Embase, and Web of Science. The search string included multiple synonyms of the following keywords: "revision TKA", "outcome" and "prognostic factor". We searched for studies assessing the association between at least one prognostic factor and at least one outcome measure after rTKA surgery. Data on sample size, study design, prognostic factors, outcomes, and the direction of the association was extracted and included in an evidence map.Aims
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Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity).Aims
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The aim of this study was to systematically compare the safety and accuracy of robot-assisted (RA) technique with conventional freehand with/without fluoroscopy-assisted (CT) pedicle screw insertion for spine disease. A systematic search was performed on PubMed, EMBASE, the Cochrane Library, MEDLINE, China National Knowledge Infrastructure (CNKI), and WANFANG for randomized controlled trials (RCTs) that investigated the safety and accuracy of RA compared with conventional freehand with/without fluoroscopy-assisted pedicle screw insertion for spine disease from 2012 to 2019. This meta-analysis used Mantel-Haenszel or inverse variance method with mixed-effects model for heterogeneity, calculating the odds ratio (OR), mean difference (MD), standardized mean difference (SMD), and 95% confidence intervals (CIs). The results of heterogeneity, subgroup analysis, and risk of bias were analyzed.Aims
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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
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The ongoing COVID-19 pandemic has disrupted and delayed medical and surgical examinations where attendance is required in person. Our article aims to outline the validity of online assessment, the range of benefits to both candidate and assessor, and the challenges to its implementation. In addition, we propose pragmatic suggestions for its introduction into medical assessment. We reviewed the literature concerning the present status of online medical and surgical assessment to establish the perceived benefits, limitations, and potential problems with this method of assessment.Aims
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Optimal exposure through the direct anterior approach (DAA) for total hip arthroplasty (THA) conducted on a regular operating theatre table is achieved with a standardized capsular releasing sequence in which the anterior capsule can be preserved or resected. We hypothesized that clinical outcomes and implant positioning would not be different in case a capsular sparing (CS) technique would be compared to capsular resection (CR). In this prospective trial, 219 hips in 190 patients were randomized to either the CS (n = 104) or CR (n = 115) cohort. In the CS cohort, a medial based anterior flap was created and sutured back in place at the end of the procedure. The anterior capsule was resected in the CR cohort. Primary outcome was defined as the difference in patient-reported outcome measures (PROMs) after one year. PROMs (Harris Hip Score (HHS), Hip disability and Osteoarthritis Outcome Score (HOOS), and Short Form 36 Item Health Survey (SF-36)) were collected preoperatively and one year postoperatively. Radiological parameters were analyzed to assess implant positioning and implant ingrowth. Adverse events were monitored.Aims
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The anterior cruciate ligament (ACL) is known to have a poor wound healing capacity, whereas other ligaments outside of the knee joint capsule such as the medial collateral ligament (MCL) apparently heal more easily. Plasmin has been identified as a major component in the synovial fluid that varies among patients. The aim of this study was to test whether plasmin, a component of synovial fluid, could be a main factor responsible for the poor wound healing capacity of the ACL. The effects of increasing concentrations of plasmin (0, 0.1, 1, 10, and 50 µg/ml) onto the wound closing speed (WCS) of primary ACL-derived ligamentocytes (ACL-LCs) were tested using wound scratch assay and time-lapse phase-contrast microscopy. Additionally, relative expression changes (quantitative PCR (qPCR)) of major LC-relevant genes and catabolic genes were investigated. The positive controls were 10% fetal calf serum (FCS) and platelet-derived growth factor (PDGF).Aims
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The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
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