Interleukin-1ß Polymorphisms Are Genetic Markers of Susceptibility to Periprosthetic Joint Infection in Total Hip and Knee Arthroplasty.

Periprosthetic joint infections (PJIs) are serious complications of prosthetic surgery. The criteria for the diagnosis of PJI integrate clinical and laboratory findings in a complex and sometimes inconclusive workflow. Host immune factors hold potential as diagnostic biomarkers in bone and joint infections. We reported that the humoral pattern-recognition molecule long pentraxin 3 (PTX3) predicts PJI in total hip and knee arthroplasty (THA and TKA, respectively). If and how genetic variation in PTX3 and inflammatory genes that affect its expression (IL-1ß, IL-6, IL-10, and IL-17A) contributes to the risk of PJI is unknown. We conducted a case-control study on a Caucasian historic cohort of THA and TKA patients who had prosthesis explant due to PJI (cases) or aseptic complications (controls). Saliva was collected from 93 subjects and used to extract DNA and genotype PTX3, IL-1ß, IL-6, IL-10, and IL-17A single-nucleotide polymorphisms (SNPs). Moreover, the concentration of IL-1ß, IL-10, and IL-6 was measured in synovial fluid and plasma. No association was found between PTX3 polymorphisms and PJI; however, the AGG haplotype, encompassing rs2853550, rs1143634, and rs1143627 in IL-1ß, was linked to the infection (p = 0.017). Also, synovial levels of all inflammatory markers were higher in cases than in controls, and a correlation emerged between synovial concentration of PTX3 and that of IL-1ß in cases only (Spearman r = 0.67, p = 0.004). We identified a relationship between rs2853550 and the synovial concentration of IL-1ß and PTX3. Our findings suggest that IL-1ß SNPs could be used for the early identification of THA and TKA patients with a high risk of infection.

Results of Utilizing Cerclage Wires in the Management of Intraoperative Vancouver B1 Fractures in Primary Total Hip Arthroplasties: A Retrospective Cohort Investigation into Clinical and Radiographic Outcomes.

BACKGROUND: Due to an increase in total hip arthroplasties (THAs), the incidence of periprosthetic hip fractures (PPHFs) is forecast to rise considerably in the next decades, with Vancouver B1 fractures (VB1) accounting for one third of total cases. Femur fixation with cerclages (with or without screws) is considered the current treatment option for intraoperative VB1. METHODS: The study retrospectively includes data from patients who developed VB1 PPHFs during THAs from 3 December 2020 to 30 November 2022. The primary outcome of this study was to identify the reintervention-free survival rate. The secondary aim was to determine clinical and radiographic assessment at follow-up, based on Harris hip score (HHS) and limb length discrepancy (LLD). RESULTS: Thirty-seven patients with a mean age of 60.03 ± 15.49 (22 to 77) years old were included. Overall, the Kaplan-Meier analysis estimated a reoperation-free survival rate of 99% (CI 95%) at 6 months. The mean limb length discrepancy (LLD) improved from -3.69 ± 6.07 (range -27.9 to 2.08) mm to 0.10 ± 0.67 (range -1.07 to 1.20) mm. The mean HHS improved from 42.72 ± 14.37 (range 21.00-96.00) to 94.40 ± 10.32 (range 56.00-100.00). CONCLUSIONS: The employment of cerclage wires represents an effective strategy for handling intraoperative VB1 fractures. Level III retrospective cohort study.

AI-based hip prosthesis failure prediction through evolutional radiological indices.

INTRODUCTION: This study aimed to develop artificial intelligence models for predicting hip implant failure from radiological features. Analyzing the evolution of the periprosthetic bone and implant's position throughout the entire follow-up period has shown the potential to be more relevant in outcome prediction than simply considering the latest radiographic images. Thus, we investigated an AI-based model employing a small set of evolutional parameters derived from conventional radiological features to predict hip prosthesis failure. MATERIALS AND METHODS: One hundred sixty-nine radiological features were annotated from historical anteroposterior and lateral radiographs for 162 total hip arthroplasty patients, 32 of which later underwent implant failure. Linear regression on each patient's chronologically sorted radiological features was employed to derive 169 corresponding evolutional parameters per image. Three sets of machine learning predictors were developed: one employing the original features (standard model), one the evolutional ones (evolutional model), and the last their union (hybrid model). Each set included a model employing all the available features (full model) and a model employing the few most predictive ones according to Gini importance (minimal model). RESULTS: The evolutional and hybrid predictors resulted highly effective (area under the ROC curve (AUC) of full models = 0.94), outperforming the standard one, whose AUC was only 0.82. The minimal hybrid model, employing just four features, three of which evolutional, scored an AUC of 0.95, proving even more accurate than the full one, exploiting 173 features. This tool could be shaped to be either highly specific (sensitivity: 80%, specificity: 98.6%) or highly sensitive (sensitivity: 90%, specificity: 92.4%). CONCLUSION: The proposed predictor may represent a highly sensitive screening tool for clinicians, capable to predict THA failure with an advance between a few months and more than a year through only four radiological parameters, considering either their value at the latest visit or their evolution through time.

Clinical and radiological outcomes of an uncemented metaphyseal short stem at minimum 10 years of follow-up: a prospective observational study.

PURPOSE: There is growing interest around short stems as they provide a less invasive approach for total hip arthroplasty preserving bone stock for a possible revision surgery if required. The main purpose of this work was to assess the long-term performance of an uncemented metaphyseal short stem in terms of survival rate and in addition its clinical and radiological outcomes. MATERIALS AND METHODS: From January to December 2010, we prospectively enrolled all consecutive patients undergoing primary total hip arthroplasty with an uncemented metaphyseal short stem in our institution with a minimum follow-up of 10 years. The radiographic features investigated were the varus-valgus stem orientation, areas of osteolysis and radiolucencies, stress shielding, and heterotopic calcifications. The clinical outcomes were measured using the HHS. RESULTS: A total of 163 patients (172 hips) were finally included and the measured survival rate at 10 years was 99.6%. The average HHS increased from 55.0 preoperatively to 97.8 at the last follow-up (p < 0.0001). Among them, 137 patients (164 hips) were also available for radiographic assessment. Mild varus malalignment was observed in 40% of cases and remained stable in the serial X-rays and was not associated with step loosening. Mild stress shielding was observed around 13 stems (9%) and moderate only around 3 implants. CONCLUSION: In conclusion, the uncemented metaphyseal short stems showed excellent survival and clinical results at 10 years and at a radiographical level very low stress shielding was observed; moreover, besides the high frequency of varus alignment the implanted stems was not associated with revision surgery or with worse clinical outcomes.

Interaction of Bacteria, Immune Cells, and Surface Topography in Periprosthetic Joint Infections.

The incidence of periprosthetic joint infections (PJIs) is ~2% of total procedures and it is expected to rise due to an ageing population. Despite the large burden PJI has on both the individual and society, the immune response to the most commonly isolated pathogens, i.e., Staphylococcus aureus and Staphylococcus epidermidis, remains incompletely understood. In this work, we integrate the analysis of synovial fluids from patients undergoing hip and knee replacement surgery with in-vitro experimental data obtained using a newly developed platform, mimicking the environment of periprosthetic implants. We found that the presence of an implant, even in patients undergoing aseptic revisions, is sufficient to induce an immune response, which is significantly different between septic and aseptic revisions. This difference is confirmed by the presence of pro- and anti-inflammatory cytokines in synovial fluids. Moreover, we discovered that the immune response is also dependent on the type of bacteria and the topography of the implant surface. While S. epidermidis seems to be able to hide better from the attack of the immune system when cultured on rough surfaces (indicative of uncemented prostheses), S. aureus reacts differently depending on the contact surface it is exposed to. The experiments we performed in-vitro also showed a higher biofilm formation on rough surfaces compared to flat ones for both species, suggesting that the topography of the implant could influence both biofilm formation and the consequent immune response.

Combining deep learning and machine learning for the automatic identification of hip prosthesis failure: Development, validation and explainability analysis.

AIM: Revision hip arthroplasty has a less favorable outcome than primary total hip arthroplasty and an understanding of the timing of total hip arthroplasty failure may be helpful. The aim of this study is to develop a combined deep learning (DL) and machine learning (ML) approach to automatically detect hip prosthetic failure from conventional plain radiographs. METHODS: Two cohorts of patients (of 280 and 352 patients) were included in the study, for model development and validation, respectively. The analysis was based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing, three images were obtained: the original image, the acetabulum image and the stem image. These images were analyzed through convolutional neural networks aiming to predict prosthesis failure. Deep features of the three images were extracted for each model and two feature-based pipelines were developed: one utilizing only the features of the original image (original image pipeline) and the other concatenating the features of the three images (3-image pipeline). The obtained features were either used directly or reduced through principal component analysis. Both support vector machine (SVM) and random forest (RF) classifiers were considered for each pipeline. RESULTS: The SVM applied to the 3-image pipeline provided the best performance, with an accuracy of 0.958 ± 0.006 in the internal validation and an F1-score of 0.874 in the external validation set. The explainability analysis, besides identifying the features of the complete original images as the major contributor, highlighted the role of the acetabulum and stem images on the prediction. CONCLUSIONS: This study demonstrated the potentialities of the developed DL-ML procedure based on plain radiographs in the detection of the failure of the hip prosthesis.

Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach.

Background: Total hip arthroplasty (THA) follow-up is conventionally conducted with serial X-ray imaging in order to ensure the early identification of implant failure. The purpose of this study is to develop an automated radiographic failure detection system. Methods: 630 patients with THA were included in the study, two thirds of which needed total or partial revision for prosthetic loosening. The analysis is based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing for proper standardization, images were analyzed through a convolutional neural network (the DenseNet169 network), aiming to predict prosthesis failure. The entire dataset was divided in three subsets: training, validation, and test. These contained transfer learning and fine-tuning algorithms, based on the training dataset, and were implemented to adapt the DenseNet169 network to the specific data and clinical problem. Results: After the training procedures, in the test set, the classification accuracy was 0.97, the sensitivity 0.97, the specificity 0.97, and the ROC AUC was 0.99. Only five images were incorrectly classified. Seventy-four images were classified as failed, and eighty as non-failed with a probability >0.999. Conclusion: The proposed deep learning procedure can detect the loosening of the hip prosthesis with a very high degree of precision.