Citations

Background: Although it is well established that the type of organism can be a risk factor for failure in patients with periprosthetic joint infection (PJI), no study to date has examined the impact of the number of positive cultures on treatment outcomes in patients undergoing 2-stage exchange. The purpose of this multicenter study was to determine the prognostic utility of multiple positive cultures at resection as a predictor of failure following reimplantation. Methods: This retrospective multicenter study identified 437 patients with chronic knee PJI who had undergone 2-stage exchange arthroplasty with a minimum of 1 year of follow-up following reimplantation. PJI was defined with use of the 2013 Musculoskeletal Infection Society (MSIS) criteria. Patients with culture-negative PJI were excluded (n = 138). Treatment failure was defined as either any reoperation for infection or PJI-related mortality. Multivariable regression controlling for risk factors for failure after a 2-stage arthroplasty was performed to determine whether ≥2 positive intraoperative cultures at resection can predict outcomes following reimplantation when compared with a single positive culture. Results: Two hundred and ninety-nine patients were included. At a mean follow-up of 6.2 ± 2.6 years, 48 patients (16.1%) experienced failure. Patients who had a failure were more likely to have had a longer interstage interval (p = 0.038) and were also more likely to have had ≥2 positive cultures at the time of resection arthroplasty (95.8% versus 75.3%; p = 0.001). On regression analysis, ≥2 positive cultures at resection was the only variable that was identified as a risk factor for failure following reimplantation in both the univariate (odds ratio [OR], 7.55 [95% CI, 2.24 to 47.0]; p = 0.006) and multivariable models (OR, 8.12 [95% CI, 2.31 to 51.9]; p = 0.005). Conclusions: This is the first study to examine the impact of the number of positive cultures on outcomes in patients with PJI. We found that the presence of ≥2 positive cultures at resection was an indicator of a poor prognosis and resulted in a greater than eightfold increase in the risk of treatment failure in patients undergoing a 2-stage exchange. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Abstract Morphological hip abnormalities (MHAs) significantly influence lifelong prognosis of the hip, contributing to early-onset osteoarthritis and impaired functionality. Developmental dysplasia of the hip (DDH) and femoroacetabular impingement (FAI) represent key pathologies, resulting from insufficient or excessive femoral head coverage, respectively. These abnormalities alter hip biomechanics, leading to structural damage, pain, and accelerated joint degeneration. Advances in genetic research have illuminated the interplay between genetics and mechanical loading in shaping hip morphology. Genes associated with osteoarthritis, DDH, and FAI include COL1A1, MMP13, and IL-6. Genes associated with FAI and osteoarthritis include ADAMTS4. Genes associated with DDH and osteoarthritis include FRZB, CX3CR1, ASPN, DKK1, PDRG1, GDF5, UQCC1, and TGF-β1. The mechanisms linking morphological derangements to symptomatic osteoarthritis remain incompletely understood. Multimodal approaches integrating imaging, biomechanics, and genetics may uncover distinct disease subtypes, enabling personalized interventions. Early detection of MHAs is critical in preventing early-onset osteoarthritis. Incorporating advanced imaging techniques, such as statistical shape modelling, can enhance the understanding of complex 3D hip morphologies and their progression to osteoarthritis. Future research should explore the genetic underpinnings of other morphologic hip conditions, including Slipped Capital Femoral Epiphysis and Legg–Calvé–Perthes disease, to refine preventive and therapeutic strategies. A comprehensive approach combining genetics, imaging, and clinical insights holds promise for mitigating the lifelong impact of MHAs.

AimsContemporary outcomes of total femoral arthroplasties (TFAs) in the non-oncological setting are limited. This study aimed to assess implant survival free from revision and reoperation, clinical outcomes, and radiological results associated with TFAs for non-oncological conditions.MethodsWe identified 24 patients who received TFAs between 1 January 2007 and 31 December 2021 through our institutional total joint registry. The patients’ mean age was 66 years (48 to 83), mean BMI was 33 kg/m2 (21 to 54), and median follow-up was four years (IQR 2 to 5). Bearings used included eight flat-faced polyethylene bearings, seven dual-mobility constructs, six constrained liners, and three face-changing liners. Of the 24 patients, 17 had a previously documented periprosthetic joint infection (PJI) and 12 were treated with chronic antibiotic suppression following their TFAs.ResultsThe two-, five-, and ten-year survivals free of any revision were 66%, 51%, and 34%, respectively. There were 12 revisions: five for hip dislocation, four for PJI, one for aseptic tibial loosening, one for prosthetic knee dislocation, and one for acetabular loosening with pelvic discontinuity. The two-, five-, and ten-year survivals free of any reoperation were 53%, 47%, and 34%, respectively. In addition to the stated TFA revisions, one patient required open reduction of a hip dislocation, and two patients required superficial irrigation and debridement procedures for wound complications. Mean Harris Hip Scores improved from a mean score of 48 to 69 (p = 0.002), and Knee Society Scores improved from a mean score of 56 to 71 (p = 0.110). At final follow-up, 20 patients had TFAs in situ, but four patients had been treated with hip disarticulation due to recalcitrant infection. All but one tibial and all non-revised acetabular components were radiologically well-fixed at final follow-up.ConclusionThe use of TFAs in the non-oncological setting, mostly for salvage of multiply revised hips with previous PJI, had a low five-year survival free from any revision of 51%, mostly for hip dislocation and infection. Nevertheless, clinical outcomes improved in this challenging cohort of patients.Cite this article: Bone Joint J 2025;107-B(4):449–454.

Abstract Purpose Developing large‐scale, standardized radiographic registries for anterior cruciate ligament (ACL) injuries with artificial intelligence (AI) tools can enhance personalized orthopaedics. We propose deploying Artificial Intelligence for Knee Imaging Registration and Analysis (AKIRA), a trio of deep learning (DL) algorithms, to automatically classify and annotate radiographs. We hypothesize that algorithms can efficiently organize radiographs based on laterality, projection, identify implants and classify osteoarthritis (OA) grade. Methods A collection of 20,836 knee radiographs from all time points of treatment (mean orthopaedic follow‐up 70.7 months; interquartile range [IQR]: 6.8–172 months) were aggregated from 1628 ACL‐injured patients (median age 26 years [IQR: 19–42], 57% male). Three DL algorithms (EfficientNet, YOLO [You Only Look Once] and Residual Network) were employed. Radiograph laterality and projection (anterior‐posterior [AP], lateral, sunrise, posterior‐anterior, hip–knee–ankle and Camp‐Coventry intercondylar [notch]) were labelled by a DL model. Manually provided labels of metal fixation implants were used to develop a DL object detection algorithm. The degree of OA, both as measured by specific Kellgren–Lawrence (KL) grades, as well as based on a binarized label of OA (defined as KL Grade ≥2), on standing AP radiographs were classified using a DL algorithm. Individual model performances were evaluated on a subset of images prior to the deployment of AKIRA to registry construction using all ACL radiographs. Results The classification algorithms showed excellent performance in classifying radiographic laterality (F1 score: 0.962–0.975) and projection (F1 score: 0.941–1.0). The object detection algorithm achieved high precision–recall (area under the precision‐recall curve: 0.695–0.992) for identifying various metal fixations. The KL classifier reached concordances of 0.39–0.40, improving to 0.81–0.82 for binary OA labels. Sequential deployment of AKIRA following internal validation processed and labelled all 20,836 images with the appropriate views, implants, and the presence of OA within 88 min. Conclusion AKIRA effectively automated the classification and object detection in a large radiograph cohort of ACL injuries, creating an AI‐enabled radiographic registry with comprehensive details on laterality, projection, implants and OA. Study Design Cross‐sectional study. Level of Evidence Level IV.

Background: The direct anterior approach (DAA) is a popular approach for primary total hip arthroplasty (THA). However, the contemporary outcomes for DAA THA need further elucidation. Therefore, we aimed to describe implant survivorship, complications, and clinical outcomes after DAA THA. Methods: From our multi-institutional total joint registry, 3,184 patients who had undergone 3,698 primary DAA THA between 2010 and 2019 were identified. The identified patients had a mean age of 65 years and a mean body mass index (BMI) of 29 kg/m 2 , and 53% of patients were female. The indications for revision and reoperation and the incidence of complications were collected and analyzed. Potential risk factors, including age, sex, BMI, and high-volume compared with low-volume operating surgeons, were examined. Descriptive statistics and Kaplan-Meier survivorship with Cox regression analyses were performed. Results: At 10 years following primary DAA THA, the cohort had 96% (95% confidence interval [CI], 95% to 98%) survivorship free of any revision and 94% (95% CI, 92% to 96%) survivorship free of any reoperation. The leading indications for revision were periprosthetic joint infection (PJI) (n = 24; 5-year cumulative incidence, 0.93% [95% CI, 0.6% to 1.5%]), periprosthetic fracture (n = 20; 5-year cumulative incidence, 0.62% [95% CI, 0.4% to 1.0%]), and aseptic loosening (n = 14; 11 femoral, 3 acetabular; 5-year cumulative incidence, 0.84% [95% CI, 0.5% to 1.5%]). A BMI of ≥40 kg/m 2 was found to be significantly associated with PJI (hazard ratio [HR], 6.4; p < 0.001), reoperation (HR, 3.5; p < 0.001), and nonoperative complications (HR, 2.3; p = 0.018). Survivorship free of recurrent instability was 99.6% (95% CI, 99.4% to 99.8%) at 5 and 10 years, and the cumulative incidence of revision for instability was 0.14% at 5 years. Conclusions: In one of the largest published series to date, survivorship following DAA THA was satisfactory at early to intermediate follow-up. The leading indications for revision were PJI, periprosthetic fracture, and aseptic loosening. Instability after DAA THA was uncommon and infrequently led to revision. As a note of caution, a BMI of ≥40 kg/m 2 was identified as a risk factor for adverse outcome after DAA THA. Level of Evidence: Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.

Abstract Deep learning is revolutionizing medical imaging analysis by enabling the classification of various pathoanatomical conditions at scale. Unfortunately, there have been a limited number of accurate and efficient machine learning (ML) algorithms that have been developed for the diagnostic workup of morphological hip pathologies, including developmental dysplasia of the hip and femoroacetabular impingement. The current study reports on the performance of a novel ML model with YOLOv5 and ConvNeXt-Tiny architecture in predicting the morphological features of these conditions, including cam deformity, ischial spine sign, dysplastic appearance, and other abnormalities. The model achieved 78.0% accuracy for detecting cam deformity, 87.2% for ischial spine sign, 76.6% for dysplasia, and 71.6% for all abnormalities combined. The model achieved an Area under the Receiver Operating Curve of 0.89 for ischial spine sign, 0.80 for cam deformity, 0.80 for dysplasia, and 0.81 for all abnormalities combined. Inter-rater agreement among surgeons, assessed using Gwet’s AC1, was substantial for dysplasia (0.83) and all abnormalities (0.88), and moderate for ischial spine sign (0.75) and cam deformity (0.61).

Abstract Periprosthetic fractures of the lesser trochanter may migrate proximally and result in anterior mechanical irritation and impingement with hip flexion refractory to comprehensive conservative management. In such cases, patients may benefit from arthroscopic resection of the displaced lesser trochanteric fragment. We present a minimally invasive, arthroscopic approach with initial access through an air bursogram. This technique can be performed percutaneously, with standard access cannulas, without the need for additional disposable instrumentation or large cannulas. In such a way, reproducible arthroscopic instrumentation can be provided for safe and efficient fragment removal while remaining cognizant of the nearby medial neurovascular bundle. Technique Video video See video under supplementary data.

While the role and benefit of perioperative intravenous (IV) antibiotics in patients undergoing total joint arthroplasty (TJA) is well-established, oral antibiotic use in TJA remains a controversial topic with wide variations in practice patterns. With this review, we aimed to better educate the orthopedic surgeon on when and how oral antibiotics may be used most effectively in TJA patients, and to identify gaps in the literature that could be clarified with targeted research. Extended oral antibiotic prophylaxis (EOAP) use in high-risk primary, aseptic revision, and exchange TJA for infection may be useful in decreasing periprosthetic joint infection (PJI) rates. When prescribing oral antibiotics either as EOAP or for draining wounds, patient factors, type of surgery, and type of infectious organisms should be considered in order to optimally prevent and treat PJI. It is important to maintain antibiotic stewardship by administering the proper duration, dose, and type of antibiotics and by consulting infectious disease when necessary.

The digitization of medical records and expanding electronic health records has created an era of “Big Data” with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.