Promising short-term outcomes of free-hand burring technique to implant second-generation metaphyseal cone in Asian knees - a case series.

BACKGROUND: The second-generation metaphyseal cone was useful in managing bone defects in revision knee arthroplasty. However, due to the anatomical constraints in Asian osteometry, the authors utilized a novel free-hand burring technique instead of cannulated reaming for bone preparation. We reported the short-term outcomes of our surgical techniques specific to Asian osteometry. METHODS: We conducted a case series by consecutively recruiting 13 female and 12 male patients (involving 25 knees), with a mean age of 71 years (range, 54-88 years). The patients underwent revision total knee arthroplasty during the period from April 2017 to June 2022. Twenty-three tibial cones and 4 femoral cones using free-hand burring technique were implanted. The mean follow-up duration was 51 months (range 18-80 months). Due to the relatively small bone size and meta-diaphyseal center mismatch in the Asian knees, the free-hand burring technique instead of the cannulated reaming technique was adopted in preparing for cone implantation. The clinical outcomes were knee ranges of motion, the Knee Society Knee scores (KSS), end-of-stem pain, infection, and the need for revision surgery. The radiological outcomes included osteointegration, fracture, and loosening. RESULTS: Mean knee range of motion improved from 83 degrees (range 0°-120°) preoperatively to 106 degrees (range 60°-125°) postoperatively (P < 0.001). Mean KSS improved significantly from 29 (range 0-70) to 69 (range 5-100) (P < 0.001). All cones were osteointegrated. One case had transient end-of-stem pain, two developed intraoperative minor femoral fractures and one suffered from recurrent infection that did not require cone revision. Cone revision-free survivorship was 100%. There was no aseptic loosening. CONCLUSIONS: The second-generation cone implanted with free-hand burring bone preparation yielded promising short-term outcomes in Asian knees.

Reducing edge loading and alignment outliers with image-free robotic-assisted unicompartmental knee arthroplasty: a case controlled study.

BACKGROUND: Survivorship of medial unicompartmental knee arthroplasty (UKA) is technique-dependent. Correct femoral-tibial component positioning associates with improved survivorship. Image-free robotic-assisted unicompartmental knee arthroplasty enables preoperative and intraoperative planning of alignment and assessment of positioning prior to execution. This study aimed to compare the radiological outcomes between robotic-assisted UKA (R-UKA) and conventional UKA (C-UKA). METHODS: This retrospective case control study involved 140 UKA (82 C-UKA and 58 R-UKA) performed at an academic institution between March 2016 to November 2020, with a mean follow-up of 3 years. Postoperative radiographs were evaluated for mechanical axis and femoral-tibial component position. Component position was measured by two methods: (1) femoral-tibial component contact point with reference to four medial-to-lateral quadrants of the tibial tray and (2) femoral-tibial component contact point deviation from the center of the tibial tray as a percentage of the tibial tray width. Baseline demographics and complications were recorded. RESULTS: There was a higher mean component deviation in C-UKA compared with R-UKA using method 2 (17.2% vs. 12.8%; P = 0.007), but no difference in proportion of zonal outliers using method 1 (4 outliers in C-UKA, 5.1% vs. 1 outlier in R-UKA, 1.8%; P = 0.403). R-UKA showed no difference in mean mechanical alignment (C-UKA 5° vs. R-UKA 5°; P = 0.250). 2-year survivorship was 99% for C-UKA and 97% for R-UKA. Mean operative time was 18 min longer for R-UKA (P < 0.001). CONCLUSION: Image-free robotic-assisted UKA had improved component medio-lateral alignment compared with conventional technique.

Preoperative weight loss interventions before total hip and knee arthroplasty: a systematic review of randomized controlled trials.

BACKGROUND: The high co-prevalence of obesity and end-stage osteoarthritis requiring arthroplasty, with the former being a risk factor for complications during arthroplasty, has led to increasing interest in employing preoperative weight loss interventions such as bariatric surgery and diet modification. However, the current evidence is conflicting, and this study aimed to investigate the effect of weight loss intervention before arthroplasty in prospective randomized controlled trials. METHODS: Four electronic databases (MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials) were searched for prospective randomized controlled trials that compared weight loss interventions with usual care from inception to October 2023 by following the PRISMA guidelines. The Cochrane risk of bias tool and GRADE framework were used to assess the quality of the studies. Meta-analyses were performed when sufficient data were available from 2 or more studies. RESULTS: Three randomized controlled trials involving 198 patients were identified. Two studies employed diet modification, and one study utilized bariatric surgery. All three studies reported significant reductions in body weight and body mass index (BMI), and intervention groups had fewer postoperative complications. There was no difference in the length of stay between the intervention group and the control group. Variable patient-reported outcome measures were used by different research groups. CONCLUSION: Weight loss intervention can achieve significant reductions in body weight and body mass index before arthroplasty, with fewer postoperative complications reported. Further studies with different populations could confirm the effect of these interventions among populations with different obesity characteristics.

Subchondral bone expansion in advanced knee osteoarthritis: Relation with radiographic severity and role in surgical decision-making.

Background: Joint space width (JSW) is a traditional imaging marker for knee osteoarthritis (OA) severity, but it lacks sensitivity in advanced cases. We propose tibial subchondral bone area (TSBA), a new CT imaging marker to explore its relationship with OA radiographic severity, and to test its performance for classifying surgical decisions between unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) compared to JSW. Methods: We collected clinical, radiograph, and CT data from 182 patients who underwent primary knee arthroplasty (73 UKA, 109 TKA). The radiographic severity was scored using Kellgren-Lawrence (KL) grading system. TSBA and JSW were extracted from 3D CT-reconstruction model. We used independent t-test to investigate the relationship between TSBA and KL grade, and binary logistic regression to identify factors associated with TKA risk. The accuracy of TSBA, JSW and established classification model in differentiating between UKA and TKA was assessed using AUC. Results: All parameters exhibited inter- and intra-class coefficients greater than 0.966. Patients with KL grade 4 had significantly larger TSBA than those with KL grade 3. TSBA (0.708 of AUC) was superior to minimal/average JSW (0.547/0.554 of AUC) associated with the risk of receiving TKA. Medial TSBA, together with gender and Knee Society Knee Score, emerged as independent classification factors in multivariate analysis. The overall AUC of composite model for surgical decision-making was 0.822. Conclusion: Tibial subchondral bone area is an independent imaging marker for radiographic severity, and is superior to JSW for surgical decision-making between UKA and TKA in advanced OA patients.

Radiomics analysis of patellofemoral joint improves knee replacement risk prediction: Data from the Multicenter Osteoarthritis Study (MOST).

Objective: Knee replacement (KR) is the last-resort treatment for knee osteoarthritis. Although radiographic evidence of tibiofemoral joint has been widely adopted for prognostication, patellofemoral joint has gained little attention and may hold additional value for further improvements. We aimed to quantitatively analyse patellofemoral joint through radiomics analysis of lateral view radiographs for improved KR risk prediction. Design: From the Multicenter Osteoarthritis Study dataset, we retrospectively retrieved the initial-visit lateral left knee radiographs of 2943 patients aged 50 to 79. They were split into training and test cohorts at a 2:1 ratio. A comprehensive set of radiomic features were extracted within the best-performing subregion of patellofemoral joint and combined into a radiomics score (RadScore). A KR risk score, derived from Kellgren-Lawrence grade (KLG) of tibiofemoral joint and RadScore of patellofemoral joint, was developed by multivariate Cox regression and assessed using time-dependent area under receiver operating characteristic curve (AUC). Results: While patellofemoral osteoarthritis (PFOA) was insignificant during multivariate analysis, RadScore was identified as an independent risk factor (multivariate Cox p-value < 0.001) for KR. The subgroup analysis revealed that RadScore was particularly effective in predicting rapid progressor (KR occurrence before 30 months) among early- (KLG < 2) and mid-stage (KLG â€‹= â€‹2) patients. Combining two joints radiographic information, the AUC reached 0.89/0.87 for predicting 60-month KR occurrence. Conclusions: The RadScore of the patellofemoral joint on lateral radiographs emerges as an independent prognostic factor for improving KR prognosis prediction. The KR risk score could be instrumental in managing progressive knee osteoarthritis interventions.

Adaptive Fusion of Deep Learning With Statistical Anatomical Knowledge for Robust Patella Segmentation From CT Images.

Kneeosteoarthritis (KOA), as a leading joint disease, can be decided by examining the shapes of patella to spot potential abnormal variations. To assist doctors in the diagnosis of KOA, a robust automatic patella segmentation method is highly demanded in clinical practice. Deep learning methods, especially convolutional neural networks (CNNs) have been widely applied to medical image segmentation in recent years. Nevertheless, poor image quality and limited data still impose challenges to segmentation via CNNs. On the other hand, statistical shape models (SSMs) can generate shape priors which give anatomically reliable segmentation to varying instances. Thus, in this work, we propose an adaptive fusion framework, explicitly combining deep neural networks and anatomical knowledge from SSM for robust patella segmentation. Our adaptive fusion framework will accordingly adjust the weight of segmentation candidates in fusion based on their segmentation performance. We also propose a voxel-wise refinement strategy to make the segmentation of CNNs more anatomically correct. Extensive experiments and thorough assessment have been conducted on various mainstream CNN backbones for patella segmentation in low-data regimes, which demonstrate that our framework can be flexibly attached to a CNN model, significantly improving its performance when labeled training data are limited and input image data are of poor quality.

Radiomics signature of osteoarthritis: Current status and perspective.

Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis. The translational potential of this article: Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.

Long-term follow-up of an uncemented proximally hydroxyapatite-coated femoral stem in total hip arthroplasty.

Aims: The aim of this study was to evaluate the survival of a collarless, straight, hydroxyapatite-coated femoral stem in total hip arthroplasty (THA) at a minimum follow-up of 20 years. Methods: We reviewed the results of 165 THAs using the Omnifit HA system in 138 patients, performed between August 1993 and December 1999. The mean age of the patients at the time of surgery was 46 years (20 to 77). Avascular necrosis was the most common indication for THA, followed by ankylosing spondylitis and primary osteoarthritis. The mean follow-up was 22 years (20 to 31). At 20 and 25 years, 113 THAs in 91 patients and 63 THAs in 55 patients were available for review, respectively, while others died or were lost to follow-up. Kaplan-Meier analysis was performed to evaluate the survival of the stem. Radiographs were reviewed regularly, and the stability of the stem was evaluated using the Engh classification. Results: A total of seven stems (4.2%) were revised during the study period: one for aseptic loosening, three for periprosthetic fracture, two for infection, and one for recurrent dislocation. At 20 years, survival with revision of the stem for any indication and for aseptic loosening as the endpoint was 96.0% (95% confidence interval (CI) 92.6 to 99.5) and 98.4% (95% CI 96.2 to 100), respectively. At 25 years, the corresponding rates of survival were 94.5% (95% CI 89.9 to 99.3) and 98.1% (95% CI 95.7 to 99.6), respectively. There was radiological evidence of stable bony fixation in 86 stems (76.1%) and evidence of loosening in four (3.5%) at 20 years. All patients with radiological evidence of loosening were asymptomatic. Conclusion: The Omnifit HA femoral stem offered promising long-term survival into the third decade.

The effect of the surgical helmet system on intraoperative contamination in arthroplasty surgery.

Aims: The surgical helmet system (SHS) was developed to reduce the risk of periprosthetic joint infection (PJI), but the evidence is contradictory, with some studies suggesting an increased risk of PJI due to potential leakage through the glove-gown interface (GGI) caused by its positive pressure. We assumed that SHS and glove exchange had an impact on the leakage via GGI. Methods: There were 404 arthroplasty simulations with fluorescent gel, in which SHS was used (H+) or not (H-), and GGI was sealed (S+) or not (S-), divided into four groups: H+S+, H+S-, H-S+, and H-S-, varying by exposure duration (15 to 60 minutes) and frequency of glove exchanges (0 to 6 times). The intensity of fluorescent leakage through GGI was quantified automatically with an image analysis software. The effect of the above factors on fluorescent leakage via GGI were compared and analyzed. Results: The leakage intensity increased with exposure duration and frequency of glove exchanges in all groups. When SHS was used and GGI was not sealed (H+S-), the leakage intensity via GGI had the fastest increase, consistently higher than other groups (H+S+, H-S+ and H-S-) after 30 minutes (p < 0.05) and when there were more than four instances of glove exchange (p < 0.05). Additionally, the leakage was strongly correlated with the duration of exposure (rs = 0.8379; p < 0.050) and the frequency of glove exchange (rs = 0.8198; p < 0.050) in H+S-. The correlations with duration and frequency turned weak when SHS was not used (H-) or GGI was sealed off (S+). Conclusion: Due to personal protection, SHS is recommended in arthroplasties. Meanwhile, it is strongly recommended to seal the GGI of the inner gloves and exchange the outer gloves hourly to reduce the risk of contamination from SHS.

Robotic arm-assisted unicondylar knee arthroplasty resulted in superior radiological accuracy: a propensity score-matched analysis.

INTRODUCTION: Unicompartmental knee arthroplasty (UKA) is an effective surgical treatment for medial compartment arthritis of the knee, yet surgical outcomes are directly related to surgical execution. Robotic arm-assisted surgery aims to address these difficulties by allowing for detailed preoperative planning, real-time intraoperative assessment and haptic-controlled bone removal. This study aimed to compare the clinical and radiological outcomes between conventional manual mobile bearing and robot arm-assisted fixed bearing medial UKA in our local population. MATERIALS AND METHODS: This is a retrospective case-control study of 148 UKAs performed at an academic institution with a minimum of 1-year follow-up. 74 robotic arm-assisted UKAs were matched to 74 conventional UKAs via propensity score matching. Radiological outcomes included postoperative mechanical axis and individual component alignment. Clinical parameters included a range of motion, Knee Society knee score and functional assessment taken before, 6 and 12 months after the operation. RESULTS: Robot arm-assisted UKA produced a more neutral component coronal alignment in both femoral component (robotic -0.2 ± 2.8, manual 2.6 ± 2.3; P = 0.043) and tibial component (robotic -0.3 ± 4.0, manual 1.7 ± 5.3; P < 0.001). While the postoperative mechanical axis was comparable, robot arm-assisted UKA demonstrated a smaller posterior tibial slope (robotic 5.7 ± 2.7, manual 8.2 ± 3.3; P = 0.02). Clinical outcomes did not show any statistically significant differences. CONCLUSION: Compared with conventional UKA, robotic arm-assisted UKA demonstrated improved component alignment and comparable clinical outcomes. Improved radiological accuracy with robotic-arm assistance demonstrated promising early results.

Prediction of Total Knee Arthroplasty Sizes with Demographics, including Hand and Foot Sizes.

Anticipating implant sizes before total knee arthroplasty (TKA) allows the surgical team to streamline operations and prepare for potential difficulties. This study aims to determine the correlation and derive a regression model for predicting TKA sizes using patient-specific demographics without using radiographs. We reviewed the demographics, including hand and foot sizes, of 1,339 primary TKAs. To allow for comparison across different TKA designs, we converted the femur and tibia sizes into their anteroposterior (AP) and mediolateral (ML) dimensions. Stepwise multivariate regressions were performed to analyze the data. Regarding the femur component, the patient's foot, gender, height, hand circumference, body mass index, and age was the significant demographic factors in the regression analysis (R-square 0.541, p < 0.05). For the tibia component, the significant factors in the regression analysis were the patient's foot size, gender, height, hand circumference, and age (R-square 0.608, p < 0.05). The patient's foot size had the highest correlation coefficient for both femur (0.670) and tibia (0.697) implant sizes (p < 0.05). We accurately predicted the femur component size exactly, within one and two sizes in 49.5, 94.2, and 99.9% of cases, respectively. Regarding the tibia, the prediction was exact, within one and two sizes in 53.0, 96.0, and 100% of cases, respectively. The regression model, utilizing patient-specific characteristics, such as foot size and hand circumference, accurately predicted TKA femur and tibia sizes within one component size. This provides a more efficient alternative for preoperative planning.

Long Cephalomedullary Nails Can Be a Cheap and Effective Interval Revision Prosthesis in Infected Hip Replacements That Require Proximal Femoral Replacement: A Small Case Series.

Hip prosthetic joint infection management is complex and expensive, especially in severe bone loss. Reducing the price of interval prosthesis when performing staged revision could minimize costs without compromising outcomes. We present 2 similar techniques developed independently that use an antibiotic-coated cephalomedullary nail with a total hip arthroplasty bearing (head and cemented acetabular component) attached to it as an interval proximal femoral replacement prosthesis. Using this technique, the femoral implant cost was reduced up to 10-fold. All patients have recovered well with resolution of infection and functional recovery similar to patients undergoing proximal femoral replacement. In one case, the lag screw (femoral neck) fractured at 5 months prompting the second-stage revision. This complication should be considered when deciding the timing of second-stage revisions in these cases.

Two-stage Partial Component Retention and Interim Cemented Liner for Infected Total Hip Arthroplasty: A Case Report.

Introduction: There is interest in partial exchange for infected total hip arthroplasty, as an alternative to complete removal of components in a traditional two-stage revision. Partial exchange avoids the difficulty of removing a well-fixed component and its associated bone loss. Case Report: We report a case of a 61-year-old male patient with an infected total hip arthroplasty, who underwent a two-stage partial exchange, with retention of the well-fixed femoral stem, and an interim cemented liner. He had excellent function and no infection recurrence at 4 years of follow-up. Conclusion: Two-stage partial exchange with interim cemented liner could be an effective option for infected total hip arthroplasty.

The association between tibial torsion, knee flexion excursion and foot progression during gait in people with knee osteoarthritis: a cross-sectional study.

BACKGROUND: Lower limb malalignment is associated with gait kinematics, but there is limited information on the relationship between gait kinematics and tibial torsion in individuals with knee osteoarthritis (OA). This study aimed to investigate possible associations between tibial torsion and early stance kinematics during gait in people with mild and moderate medial knee OA. METHODS: Forty-seven participants (age: 62.1 ± 6.0 years; female/male: 37/10) diagnosed with medial knee OA were recruited from a regional hospital. Thirty of them had mild and seventeen had moderate knee OA. Lower limb alignment including tibial torsion and valgus/varus alignment were assessed by an EOS biplaner X-ray system with participants in weight-bearing position. Lower limb kinematics during gait was captured using the Vicon motion analysis system. The associations were estimated by partial Pearson correlation coefficient test. RESULTS: Our results indicated that external tibial torsion was related to early stance knee flexion excursion in participants with moderate knee OA (r = -0.58, p = 0.048), but not in participants with mild knee OA (r = 0.34, p = 0.102). External tibial torsion was associated with external foot progression angle (r = 0.48, p = 0.001), and knee varus/valgus alignment was associated with knee flexion excursion (r = -0.39, p = 0.010) in all participants. CONCLUSIONS: Both horizontal and frontal lower limb alignments were associated with knee flexion excursion at early stance of gait cycle in participants with medial knee OA. The distal rotational profile of lower limb would likely affect knee motion in sagittal plane. It implies that people with moderate knee OA could possibly benefit from correction of rotational alignment of lower limb.

The role of telemedicine in joint replacement surgery? An updated review.

The usage of telemedicine and telehealth services has grown tremendously and has become increasingly relevant and essential. Technological advancements in current telehealth services have supported its use as a viable alternative tool to conduct visits for consultations, follow-up, and rehabilitation in total joint arthroplasty. Such technology has been widely implemented, particularly during the coronavirus 2019 (COVID-19) pandemic, to deliver postoperative rehabilitation among patients receiving total joint arthroplasty (TJA), further demonstrating its feasibility with a lower cost yet comparable clinical outcomes when compared with traditional care. There remains ample potential to utilize telemedicine for prehabilitation to optimize the preoperative status and postoperative outcomes of patients with osteoarthritis. In this review, various implementations of telemedicine within total joint arthroplasty and future application of telemedicine to deliver tele-prehabilitation in TJA are discussed.

Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review.

BACKGROUND: Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection. METHODS: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their 'black box' nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified. RESULTS: Eleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis. CONCLUSION: Machine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings.

Metal-on-crosslinked polyethylene in total hip arthroplasty - an excellent combination at fifteen to twenty years of follow-up.

PURPOSE: Cross-linked polyethylene (PE) has been used with great clinical success in total hip arthroplasty (THA) since its debut in the late 1990's. However, reports regarding this bearing couple near the end of its second decade of service are still scant. The aim of this study was to first determine the long term clinical and radiological results and second Investigate what factors affect wear rates using a metal-on-crosslinked PE bearing articulation. METHODS: 55 THAs using a single brand of cross-linked liner, cementless cup and 28 mm hip ball were performed in 44 patients. Age, sex, Charlson Comorbidity Index (CCI) and need for revision surgery were recorded. Linear and volumetric wear was determined using the Martell method. RESULTS: Mean age at operation was 51.2 (29-73 ± 12.1) years. Mean duration of follow-up was 16.9 years (range 15.0-20.1 ± 1.1 years). Osteolysis was not present in the latest follow-up radiographs. Median linear and volumetric wear rate was 0.038 mm/year (95% CI 0.032-0.047) and 7.115mm3/year (95% CI 6.92-17.25) respectively. Acetabular component position was not found to be related to both linear and volumetric wear. No significant difference was found in the linear and volumetric wear rates of thinner and thicker liners (8 mm or below and > 8 mm) (p = 0.849 and p = 0.64 respectively). CONCLUSION: Metal-on-crosslinked PE is associated with low linear and volumetric wear rates which has virtually obviated osteolysis and has translated to excellent survivorship even at long term follow up. In-vivo oxidation does not appear to be of clinical concern at this point.

Isolated Liner Exchange in Total Hip Arthroplasty at a Mean of 13 Years of Follow-up: Does Fixation Technique Matter?

BACKGROUND: Isolated liner exchange is an option to address polyethylene wear after total hip arthroplasty (THA). The liner can be fixed with either the original locking mechanism or cemented into the acetabular cup. Whether the method used for liner fixation has any bearing on the outcomes in the first and second decade after surgery is still unclear. METHODS: Data for all patients who had undergone isolated liner exchange surgery in our institution between April 1995 and January 2015 were retrieved. Patients were classified according to the type of polyethylene liner (conventional or highly crosslinked polyethylene) and the locking mechanism used (original locking mechanism or cemented). Survivorship and revision rates were compared among different subgroups. A total of 118 isolated liner exchanges were performed and patients had a mean duration of follow-up of 13 years (range, 5 to 25). RESULTS: Overall estimated mean survivorship was 17 years. Use of highly crosslinked polyethylene (HXLPE) had a lower re-revision rate compared to conventional liners (10.5 versus 46.9%) (P < .001). The re-revision rate of exchanges using HXLPE was not affected by the type of fixation (original locking mechanism 11.1 versus cement 10.0%, P = .868). Conversely, using the original locking mechanism with a conventional liner had a higher re-revision rate compared to cemented conventional liners (58.3 versus 12.5%) (P = .024). CONCLUSION: HXLPE liners should be used in insert exchange surgery whenever possible. Re-revision rate of exchanges using HXLPE was not affected by the fixation technique used. Cementing an insert into an acetabular component is associated with good survivorship at a mean of 13 years follow-up.

Risk factors associated with surgical site infections following joint replacement surgery: a narrative review.

BACKGROUND: Surgical site infection following joint replacement surgery is still a significant complication, resulting in repeated surgery, prolonged antibiotic therapy, extended postoperative hospital stay, periprosthetic joint infection, and increased morbidity and mortality. This review discusses the risk factors associated with surgical site infection. RELATED RISK FACTORS: The patient-related factors include sex, age, body mass index (BMI), obesity, nutritional status, comorbidities, primary diagnosis, living habits, and scores of the American Society of Anesthesiologists physical status classification system, etc. Surgery-related factors involve preoperative skin preparation, prolonged duration of surgery, one-stage bilateral joint replacement surgery, blood loss, glove changes, anti-microbial prophylaxis, topical anti-bacterial preparations, wound management, postoperative hematoma, etc. Those risk factors are detailed in the review. CONCLUSION: Preventive measures must be taken from multiple perspectives to reduce the incidence of surgical site infection after joint replacement surgery.

Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review.

BACKGROUND: Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty. METHODS: PubMed and EMBASE databases were searched for articles published in peer-reviewed journals between January 1, 2010 and May 31, 2021. The terms included: 'artificial intelligence', 'machine learning', 'knee', 'osteoarthritis', and 'arthroplasty'. We selected studies focusing on the use of AI in diagnosis of knee osteoarthritis, prediction of the need for total knee arthroplasty, and prediction of outcomes of total knee arthroplasty. Non-English language articles and articles with no English translation were excluded. A reviewer screened the articles for the relevance to the research questions and strength of evidence. RESULTS: Machine learning models demonstrated promising results for automatic grading of knee radiographs and predicting the need for total knee arthroplasty. The artificial intelligence algorithms could predict postoperative outcomes regarding patient-reported outcome measures, patient satisfaction and short-term complications. Important weaknesses of current artificial intelligence algorithms included the lack of external validation, the limitations of inherent biases in clinical data, the requirement of large datasets in training, and significant research gaps in the literature. CONCLUSIONS: Artificial intelligence offers a promising solution to improve detection and management of knee osteoarthritis. Further research to overcome the weaknesses of machine learning models may enhance reliability and allow for future use in routine healthcare settings.