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The functional outcomes of periacetabular osteotomy (PAO) and factors predicting outcome in the older patient with acetabular dysplasia are not well understood. We therefore retrospectively determined the functional outcome of 70 patients (87 hips) over age 40 treated with PAO in three institutions; we also determined whether preoperative factors, particularly the presence of osteoarthritis, influenced the survival of the hip or time to total hip arthroplasty after PAO. The average age at surgery was 43.6 years. The minimum followup was 2 years (mean, 4.9 years; range, 2-13 years). Twenty-one hips (24%) had undergone total hip arthroplasty (THA), at a mean of 5.2 years after PAO (range, 1.9-7.6 years). Surviving hips had a mean improvement in Harris hip score from 60.7 to 90.3 and in total WOMAC pain score from 8.7 to 3. We observed no differences in preoperative or postoperative radiographic measurements or preoperative clinical function scores (HHS, WOMAC) in hips surviving and hips having THA. The risk of THA at 5 years after PAO was 12% in hips with preoperative Tönnis Grade 0 or 1 and 27% for Tönnis Grade 2. Our preliminary study suggests that PAO will give satisfactory functional and pain scores in patients over age 40 having dysplastic hips with mild or no arthrosis. [ABSTRACT FROM AUTHOR]

Background: Loosening of a cemented glenoid component is an important cause of failure in shoulder arthroplasty. This study was developed to examine the outcome of patients managed with a metal-backed, bone-ingrowth glenoid component as an alternative to a cemented component.Methods: The study group included eighty-three total shoulder arthroplasties with a metal-backed, bone-ingrowth glenoid component performed between 1989 and 1994. Seventy-four shoulders had a diagnosis of primary osteoarthritis, and nine shoulders had other diagnoses. All patients were followed radiographically and clinically for a minimum of two years or until the time of revision surgery. Kaplan-Meier survival estimates were performed with revision and/or radiographic failure as the end points.Results: The mean clinical follow-up was 9.5 years, and the mean radiographic follow-up was 7.1 years. Pain ratings (on a scale of 1 to 5) decreased from a mean of 4.7 preoperatively to 2.0 postoperatively. The mean range of motion in active elevation increased from 102 degrees preoperatively to 135 degrees postoperatively; the mean external rotation increased from 27 degrees to 56 degrees . Glenohumeral joint instability developed in fourteen shoulders. Radiographic changes consistent with glenoid component loosening were present in thirty-three shoulders. Polyethylene wear with metal wear of the glenoid component was noted in twenty-one shoulders, and humeral component loosening was seen in fifteen shoulders. Revision procedures were performed in twenty-six shoulders. There were no identifiable patient, disease, or surgical characteristics associated with failure, either clinically or radiographically. The five-year survival estimate free of revision or radiographic failure was 79.9% (95% confidence interval, 71.6% to 89.3%), and the ten-year survival estimate was 51.9% (95% confidence interval, 41.0% to 65.8%).Conclusions: The high rate of failure of total shoulder arthroplasties performed with this metal-backed, bone-ingrowth glenoid component raises concerns as to its use, and perhaps the use of other types of metal-backed components, in shoulder arthroplasty, other than for special situations.Level Of Evidence: Therapeutic Level IV. See Instructions to Authors for a complete description of levels of evidence. [ABSTRACT FROM AUTHOR]

Aims: Distal femoral osteotomies (DFOs) are commonly used for the correction of valgus deformities and lateral compartment osteoarthritis. However, the impact of a DFO on subsequent total knee arthroplasty (TKA) function remains a subject of debate. Therefore, the purpose of this study was to determine the effect of a unilateral DFO on subsequent TKA function in patients with bilateral TKAs, using the contralateral knee as a self-matched control group., Methods: The inclusion criteria consisted of patients who underwent simultaneous or staged bilateral TKA after prior unilateral DFO between 1972 and 2023. The type of osteotomy performed, osteotomy hardware fixation, implanted TKA components, and revision rates were recorded. Postoperative outcomes including the Forgotten Joint Score-12 (FJS-12), Tegner Activity Scale score, and subjective knee preference were also obtained at final follow-up., Results: A total of 21 patients underwent bilateral TKA following unilateral DFO and were followed for a mean of 31.5 years (SD 11.1; 20.2 to 74.2) after DFO. The mean time from DFO to TKA conversion was 13.1 years (SD 9.7) with 13 (61.9%) of DFO knees converting to TKA more than ten years after DFO. There was no difference in arthroplasty implant systems employed in both the DFO-TKA and TKA-only knees (p > 0.999). At final follow-up, the mean FJS-12 of the DFO-TKA knee was 62.7 (SD 36.6), while for the TKA-only knee it was 65.6 (SD 34.7) (p = 0.328). In all, 80% of patients had no subjective knee preference or preferred their DFO-TKA knee. Three DFO-TKA knees and two TKA-only knees underwent subsequent revision following index arthroplasty at a mean of 12.8 years (SD 6.9) and 8.5 years (SD 3.8), respectively (p > 0.999)., Conclusion: In this self-matched study, DFOs did not affect subsequent TKA function as clinical outcomes, subjective knee preference, and revision rates were similar in both the DFO-TKA and TKA-only knees at mean 32-year follow-up., Competing Interests: M. Hevesi reports a consulting fee from Vericel; and payment for educational events from Smith & Nephew, Arthrex, Medwest Associates, and Foundation Medical, all of which are unrelated to this work. A. J. Krych discloses royalties from and being a paid consultant for Arthrex; and having a leadership, fiduciary, or board role for the American Journal of Sports Medicine, Arthroscopy Association of North America, International Cartilage Repair Society, and Springer, all of which are unrelated to this paper. D. B. F. Saris declares being a paid consultant for Phoenix Kinetic and Relive Biotechnologies; and having a leadership, fiduciary, or board role for the American Academy of Orthopaedic Surgeons (AAOS), the Institute of Corporate Responsibility and Sustainability, and Cartilage, all of which are also unrelated. M. J. Taunton reports receiving royalties and consulting fees from Enovis; receiving publishing royalties from the Journal of Arthroplasty; and having a leadership, fiduciary, or board role for AAOS and the Journal of Arthroplasty, all of which are unrelated to this work., (© 2024 Clark et al.)

Background: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardware., Methods: Annotators retrospectively classified 26,000 knee images detailing presence, laterality, prostheses, and radiographic views. They further annotated surgical construct locations in 11,841 knee radiographs. An uncertainty-aware multilabel EfficientNet-based classifier was trained to identify the knee laterality, implants, and radiographic view. A classifier trained with embeddings from the EfficientNet model detected out-of-domain images. An object detection model was trained to identify 20 different knee implants. Model performance was assessed against a held-out internal and an external dataset using per-class F1 score, accuracy, sensitivity, and specificity. Conformal prediction was evaluated with marginal coverage and efficiency., Results: Classification Model with Conformal Prediction: F1 scores for each label output > 0.98. Coverage of each label output was > 0.99 and the average efficiency was 0.97. Domain Detection Model:The F1 score was 0.99, with precision and recall for knee radiographs of 0.99. Object Detection Model:Mean average precision across all classes was 0.945 and ranged from 0.695 to 1.000. Average precision and recall across all classes were 0.950 and 0.886., Conclusions: We present a multilabel classifier with domain detection and an object detection model to characterize knee radiographs. Conformal prediction enhances transparency in cases when the model is uncertain., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Study Objective: Describe dosing of local anesthetic when both a periarticular injection (PAI) and peripheral nerve block (PNB) are utilized for knee arthroplasty analgesia, and compare the dosing of local to suggested maximum dosing, and look for evidence of local anesthetic systemic toxicity (LAST)., Design: A single center retrospective cohort study between May 2018 and November 2022., Setting: A major academic hospital., Patients: Patients who had both a PAI and PNB while undergoing primary, revision, total, partial, unilateral, or bilateral knee arthroplasty., Interventions: None., Measurements: Calculate the dose of local anesthetic given via PAI, PNB, and both routes combined as based on lean body weight and compare that to the suggested maximum dosing. Look for medications, clinical interventions, and critical event notes suggestive of a LAST event., Main Results: There were 4527 knee arthroplasties where both a PAI and PNB were performed during the study period. When combining PAI and PNB doses, >75% of patients received more than the suggested maximum dose of 3 mg/kg lean body weight. The median local anesthetic dosing over the study period, 4.4 mg/kg (IQR 3.5,5.9), was 147% of the suggested maximum dose (IQR 117,197). There was no conclusive evidence of LAST among any of the patients in the study., Conclusions: Over the course of our study, we had 4527 knee arthroplasties with a median PAI and PNB local anesthetic dose that was 147% of the suggested maximum without any clear clinical evidence of a LAST event., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. Understanding these biases is crucial for the development of equitable healthcare technologies. This study employs generative deep learning technology to explore and understand radiographic differences based on race among patients undergoing total hip arthroplasty., Methods: Utilizing a large institutional registry, we retrospectively analyzed pelvic radiographs from total hip arthroplasty patients, characterized by demographics and image features. Denoising diffusion probabilistic models generated radiographs conditioned on demographic and imaging characteristics. Fréchet Inception Distance assessed the generated image quality, showing the diversity and realism of the generated images. Sixty transition videos were generated that showed transforming White pelvises to their closest African American counterparts and vice versa while controlling for patients' sex, age, and body mass index. Two expert surgeons and 2 radiologists carefully studied these videos to understand the systematic differences that are present in the 2 races' radiographs., Results: Our data set included 480,407 pelvic radiographs, with a predominance of White patients over African Americans. The generative denoising diffusion probabilistic model created high-quality images and reached an Fréchet Inception Distance of 6.8. Experts identified 6 characteristics differentiating races, including interacetabular distance, osteoarthritis degree, obturator foramina shape, femoral neck-shaft angle, pelvic ring shape, and femoral cortical thickness., Conclusions: This study demonstrates the potential of generative models for understanding disparities in medical imaging data sets. By visualizing race-based differences, this method aids in identifying bias in downstream tasks, fostering the development of fairer healthcare practices., (© 2024 The Authors.)

Background: Porous metaphyseal cones are frequently utilized during revision total knee arthroplasty (TKA) procedures. The purpose of the present study was to evaluate 3D-printed metaphyseal cones used for revision TKA, with specific emphasis on implant survivorship, radiographic appearance, and clinical outcomes after short-term follow-up., Methods: We identified 740 cones (498 tibial, 242 femoral) that had been inserted during 533 revision TKA procedures that had been performed at a single tertiary care academic institution. Aseptic loosening (n = 199), periprosthetic joint infection (PJI) (n = 183), and instability (n = 84) were the most common reasons for the index revision. Type-2B or 3 bone loss was present in 67% of the knees. The mean age was 66 years, the mean body mass index was 34 kg/m 2 , and 55% of the patients were female. Serial radiographs were reviewed. The mean duration of follow-up was 4 years., Results: The 5-year cumulative incidence of cone revision for aseptic loosening was 1% (95% confidence interval [CI], 0% to 3%). The 5-year cumulative incidence of any cone revision or removal was 6% (95% CI, 4% to 9%). In total, 37 cones (19 femoral, 18 tibial) were revised, with PJI (n = 23; 14 recurrent), periprosthetic femoral fracture (n = 3), arthrofibrosis (n = 3), and aseptic loosening of the femoral component and femoral cone (n = 3) as the most common reasons for cone revision. The 5-year cumulative incidence of any TKA re-revision was 14% (95% CI, 11% to 18%). There were a total of 67 re-revisions, with PJI (n = 38), persistent drainage (n = 6), and extensor mechanism disruption (n = 3) being the most common reasons. Radiographically, 2 unrevised femoral cones and 1 unrevised tibial cone appeared to be possibly loose; all 3 cones were in knees with distal femoral replacements or hinged constructs., Conclusions: This large series showed that 3D-printed titanium metaphyseal cones were very durable at short-term follow-up, with a 5-year cumulative incidence of cone revision for aseptic loosening of 1%. PJI was the most common reason for cone revision and any revision., Level of Evidence: Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence., Competing Interests: Disclosure: The Andrew A. and Mary S. Sugg Professorship in Orthopedic Research made the research possible through its philanthropic support of Dr. Matthew P. Abdel. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article ( http://links.lww.com/JBJS/I59 ), (Copyright © 2024 by The Journal of Bone and Joint Surgery, Incorporated.)

Background: Soft tissue management in total hip arthroplasty includes appropriate restoration and/or alteration of leg length (LL) and offset to re-establish natural hip biomechanics. The purpose of this study was to evaluate the effect of LL and offset-derived variables in a multivariable survival model for dislocation., Methods: Clinical, surgical, and radiographic data was retrospectively acquired for 12,582 patients undergoing primary total hip arthroplasty at a single institution from 1998 to 2018. There were twelve variables derived from preoperative and postoperative radiographs related to LL and offset that were measured using a validated automated algorithm. These measurements, as well as other modifiable and nonmodifiable surgical, clinical, and demographic factors, were used to determine hazard ratios for dislocation risk., Results: None of the LL or offset variables conferred significant risk or protective benefit for dislocation risk. By contrast, all other variables included in the multivariable model demonstrated a statistically significant effect on dislocation risk with a minimum effect size of 28% (range 0.72 to 1.54) (sex, surgical approach, acetabular liner type, femoral head size, neurologic disease, spine disease, and prior spine surgery)., Conclusions: Contrary to traditional teaching and our hypothesis, operative changes in LL and offset did not demonstrate any clinically or statistically significant effect in this large and well-characterized cohort. This does not imply that these variables are not important in individual cases, but rather suggests the overall impact of LL and offset changes is relatively minor for dislocation risk compared to other variables that were found to be highly clinically and statistically significant in this population. These results may also suggest that surgeons do a good job of restoring native LL and offset for patients, which may mitigate their analyzed impact., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Achieving adequate alignment has traditionally been an important goal in total knee arthroplasty to achieve long-term implant survival. While accelerometer-based hand-held navigation systems (ABN) has been introduced as a way to achieve alignment, there is a limited body of evidence on its accuracy, especially in patients under 65 years with differing etiologies for knee arthritis. This study aimed to assess the precision of a specific ABN system in restoring the mechanical axis and report surgical variables and complications, with particular attention to younger patients., Methods: We conducted a retrospective review of 310 primary TKA performed with ABN from May 2016 to February 2021. The mean patient age was 67.4 (SD 8.9) years, with 43% under 65 years and mean body mass index of 33.2 (SD 6.8). The average surgical time was 96.8 min (57-171) and the average follow-up was 3.3 years (1.9-6.7). Data regarding length of stay, pain, range of motion (ROM), complications, and reinterventions were collected from the institutional joint arthroplasty registry and the medical records. Preoperative mechanical axis measurements and postoperative radiological data, including mechanical axis, component alignment and mechanical alignment outliers were analyzed., Results: The mean preoperative mechanical axis was 175.4° (SD 7.6), with 248 knees (80%) in preoperative varus. The mean postoperative mechanical axis was 179.5° (SD 1.96) with 98% of knees falling within ± 3° of the neutral mechanical axis. Only 6 knees (2 varus, 4 valgus) fell outside the ± 3° range. And 3 knees (1 varus, 2 valgus) fell outside the ± 5° range. In the sagittal plane, 296 knees (95.5%) knees were within ± 3° of goal of 3 degrees of femoral flexion and 302 (97.4%) knees were within ± 2° of goal 1° of slope for tibial component. Far outliers (alignment outside ± 5° of targeted position) were found in 3 knees. Factors such as posttraumatic arthrosis, previous surgery, presence of retained hardware, and age below 65 years were not associated with increase in alignment outliers and far outliers. No complications related to the navigation system were observed. There were 22 complications and 20 reoperations, including 2 revisions for periprosthetic joint infection and 1 revision for flexion instability. Patients that required knee manipulation achieved an ultimate flexion of 110° (SD 14.1)., Conclusions: The ABN system proved to be user-friendly and accurate in reducing alignment outliers in both coronal and sagittal planes in all patient populations. It offers a straightforward navigation solution while preserving surgeon autonomy and the use of traditional surgical tools. These findings advocate for the integration of this navigation system as a valuable tool to enhance the precision of TKA surgery in all patient groups., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Previous studies have suggested that wound complications may differ by surgical approach after total hip arthroplasty (THA), with particular attention toward the direct anterior approach (DAA). However, there is a paucity of data documenting wound complication rates by surgical approach and the impact of concomitant patient factors, namely body mass index (BMI). This investigation sought to determine the rates of wound complications by surgical approach and identify BMI thresholds that portend differential risk., Methods: This multicenter study retrospectively evaluated all primary THA patients from 2010 to 2023. Patients were classified by skin incision as having a laterally based approach (posterior or lateral approach) or DAA (longitudinal incision). We identified 17,111 patients who had 11,585 laterally based (68%) and 5,526 (32%) DAA THAs. The mean age was 65 years (range, 18 to 100), 8,945 patients (52%) were women, and the mean BMI was 30 (range, 14 to 79). Logistic regression and cut-point analyses were performed to identify an optimal BMI cutoff, overall and by approach, with respect to the risk of wound complications at 90 days., Results: The 90-day risk of wound complications was higher in the DAA group versus the laterally based group, with an absolute risk of 3.6% versus 2.6% and a multivariable adjusted odds ratio of 1.5 (P < .001). Cut-point analyses demonstrated that the risk of wound complications increased steadily for both approaches, but most markedly above a BMI of 33., Conclusions: Wound complications were higher after longitudinal incision DAA THA compared to laterally based approaches, with a 1% higher absolute risk and an adjusted odds ratio of 1.5. Furthermore, BMI was an independent risk factor for wound complications regardless of surgical approach, with an optimal cut-point BMI of 33 for both approaches. These data can be used by surgeons to help consider the risks and benefits of approach selection., Level of Evidence: Level III., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Intraprosthetic dissociation (IPD) is a complication unique to dual mobility (DM) implants where the outer polyethylene head dissociates from the inner femoral head. Increasing reports of IPD at the time of closed reduction of large head DM dislocations prompted this biomechanical study evaluating the assembly and dissociation forces of DM heads., Methods: We tested 17 polyethylene DM heads from 5 vendors. Of the heads, 12 were highly cross-linked polyethylene (4 vendors) and 5 were infused with vitamin E (2 vendors). Heads were between 46 and 47 mm in diameter, accepting a 28 mm-inner ceramic head. Implants were assembled and disassembled using a servohydraulic machine that recorded the forces and torques applied during testing. Dissociation was tested via both axial pull-out and lever-out techniques, where lever-out simulated stem-on-acetabular component impingement., Results: The initial maximum assembly force was significantly different between all vendors (P < .01) and decreased for all implants with subsequent assembly. Vendor 4-E (Link with vitamin E) heads required the highest assembly force (1,831.9 ± 81.95 N), followed by Vendor 3 (Smith & Nephew), Vendor 5 (DePuy Synthes), Vendor 1-E (Zimmer Biomet with vitamin E), Vendor 2 (Stryker), and Vendor 1 (Zimmer Biomet Arcom). Vendor 4-E implants showed the greatest dissociation resistance in both pull-out (2,059.89 N, n = 1) and lever-out (38.95 ± 2.79 Nm) tests. Vendor 1-E implants with vitamin E required higher assembly force, dissociation force, and energy than Vendor 1 heads without vitamin E., Conclusions: There were notable differences in DM assembly and dissociation forces between implants. Diminishing force was required for assembly with each additional trial across vendors. Vendor 4-E DM heads required the highest assembly and dissociation forces. Vitamin E appeared to increase the assembly and dissociation forces. Based on these results, DM polyethylene heads should not be reimplanted after dissociation, and there may be a role for establishing a minimum dissociation energy standard to minimize IPD risk., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Our previously reported randomized clinical trial of direct anterior approach (DAA) versus mini-posterior approach (MPA) total hip arthroplasty showed slightly faster initial recovery for patients who had a DAA and no differences in complications or clinical or radiographic outcomes beyond 8 weeks. The aims of the current study were to determine if early advantages of DAA led to meaningful clinical differences beyond 5 years and to identify differences in midterm complications., Methods: Of the 101 original patients, 93 were eligible for follow-up at a mean of 7.5 years (range, 2.1 to 10). Clinical outcomes were compared with Harris Hip, 12-Item Short Form Health Survey, and Hip Disability and Osteoarthritis Outcomes Scores (HOOS) scores and subscores, complications, reoperations, and revisions., Results: Harris Hip scores were similar (95.3 ± 6.0 versus 93.5 ± 10.3 for DAA and MPA, respectively, P = .79). The 12-Item Short Form Health Survey physical and mental scores were similar (46.2 ± 9.3 versus 46.2 ± 10.6, P = .79, and 52.3 ± 7.1 versus 55.2 ± 4.5, P = .07 in the DAA and MPA groups, respectively). The HOOS scores were similar (97.4 ± 7.9 versus 96.3 ± 6.7 for DAA and MPA, respectively, P = .07). The HOOS quality of life subscores were 96.9 ± 10.8 versus 92.3 ± 16.0 for DAA and MPA, respectively (P = .046). No clinical outcome met the minimally clinically important difference. There were 4 surgical complications in the DAA group (1 femoral loosening requiring revision, 1 dislocation treated closed, and 2 wound dehiscences requiring debridement), and 6 surgical complications in the MPA group (3 dislocations, 2 treated closed, and 1 revised to dual mobility; 2 intraoperative fractures treated with a cable; and 1 wound dehiscence treated nonoperatively)., Conclusions: At a mean of 7.5 years, this randomized clinical trial demonstrated no clinically meaningful differences in outcomes, complications, reoperations, or revisions between DAA and MPA total hip arthroplasty., Level of Evidence: IV., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights. AI methodologies have become more prevalent in orthopaedic research. This review will first describe an overview of AI in the medical field, followed by a description of the 3 arthroplasty research areas in which AI is commonly used (risk modeling, automated radiographic measurements, arthroplasty registry construction). Finally, we will discuss the next frontier of AI research focusing on model deployment and uncertainty quantification., (© 2024 The Authors.)

Aims: Dual-mobility (DM) components are increasingly used to prevent and treat dislocation after total hip arthroplasty (THA). Intraprosthetic dissociation (IPD) is a rare complication of DM that is believed to have decreased with contemporary implants. This study aimed to report incidence, treatment, and outcomes of contemporary DM IPD., Methods: A total of 1,453 DM components were implanted at a single academic institution between January 2010 and December 2021: 695 in primary and 758 in revision THA. Of these, 49 presented with a dislocation of the large DM head and five presented with an IPD. At the time of closed reduction of the large DM dislocation, six additional IPDs occurred. The mean age was 64 years (SD 9.6), 54.5% were female (n = 6), and mean follow-up was 4.2 years (SD 1.8). Of the 11 IPDs, seven had a history of instability, five had abductor insufficiency, four had prior lumbar fusion, and two were conversions for failed fracture management., Results: The incidence of IPD was 0.76%. Of the 11 IPDs, ten were missed either at presentation or after attempted reduction. All ten patients with a missed IPD were discharged with a presumed reduction. The mean time from IPD to surgical treatment was three weeks (0 to 23). One patient died after IPD prior to revision. Of the ten remaining hips with IPD, the DM head was exchanged in two, four underwent acetabular revision with DM exchange, and four were revised to a constrained liner. Of these, five (50%) underwent reoperation at a mean 1.8 years (SD 0.73), including one additional acetabular revision. No patients who underwent initial acetabular revision for IPD treatment required subsequent reoperation., Conclusion: The overall rate of IPD was low at 0.76%. It is essential to identify an IPD on radiographs as the majority were missed at presentation or after iatrogenic dissociation. Surgeons should consider acetabular revision for IPD to allow conversion to a larger DM head, and take care to remove impinging structures that may increase the risk of subsequent failure., Competing Interests: K. E. Mallett reports no receipt of funding, no industry or personal financial support, and leadership or fiduciary role in The American Academy of Orthopedic Surgeons. M. J. Taunton reports receipt of royalties and licences, consulting fees, and patents planned, issued, or pending from ENOVIS, and leadership or fiduciary role in The Journal of Arthroplasty. R. J. Sierra reports receipt of royalties or licences from Zimmer Biomet, OrthAlign, and Link, consulting fees from OrthAlign and Eversana, patents planned, issued, or pending from Zimmer Biomet, leadership or fiduciary role in AAHKS, The Hip Society, and The Muller Foundation, and other financial or non-financial interests in Arthrex and The Hip Society., (© 2024 The British Editorial Society of Bone & Joint Surgery.)

Purpose: The purpose of this review is to evaluate the current status of research on the application of artificial intelligence (AI)-based three-dimensional (3D) templating in preoperative planning of total joint arthroplasty., Methods: This scoping review followed the PRISMA, PRISMA-ScR guidelines, and five stage methodological framework for scoping reviews. Studies of patients undergoing primary or revision joint arthroplasty surgery that utilised AI-based 3D templating for surgical planning were included. Outcome measures included dataset and model development characteristics, AI performance metrics, and time performance. After AI-based 3D planning, the accuracy of component size and placement estimation and postoperative outcome data were collected., Results: Nine studies satisfied inclusion criteria including a focus on computed tomography (CT) or magnetic resonance imaging (MRI)-based AI templating for use in hip or knee arthroplasty. AI-based 3D templating systems reduced surgical planning time and improved implant size/position and imaging feature estimation compared to conventional radiographic templating. Several components of data processing and model development and testing were insufficiently covered in the studies included in this scoping review., Conclusions: AI-based 3D templating systems have the potential to improve preoperative planning for joint arthroplasty surgery. This technology offers more accurate and personalized preoperative planning, which has potential to improve functional outcomes for patients. However, deficiencies in several key areas, including data handling, model development, and testing, can potentially hinder the reproducibility and reliability of the methods proposed. As such, further research is needed to definitively evaluate the efficacy and feasibility of these systems., (© 2024. The Author(s) under exclusive licence to SICOT aisbl.)

Background: Revision total hip arthroplasty (THA) requires preoperatively identifying in situ implants, a time-consuming and sometimes unachievable task. Although deep learning (DL) tools have been attempted to automate this process, existing approaches are limited by classifying few femoral and zero acetabular components, only classify on anterior-posterior (AP) radiographs, and do not report prediction uncertainty or flag outlier data., Methods: This study introduces Total Hip Arhtroplasty Automated Implant Detector (THA-AID), a DL tool trained on 241,419 radiographs that identifies common designs of 20 femoral and 8 acetabular components from AP, lateral, or oblique views and reports prediction uncertainty using conformal prediction and outlier detection using a custom framework. We evaluated THA-AID using internal, external, and out-of-domain test sets and compared its performance with human experts., Results: THA-AID achieved internal test set accuracies of 98.9% for both femoral and acetabular components with no significant differences based on radiographic view. The femoral classifier also achieved 97.0% accuracy on the external test set. Adding conformal prediction increased true label prediction by 0.1% for acetabular and 0.7 to 0.9% for femoral components. More than 99% of out-of-domain and >89% of in-domain outlier data were correctly identified by THA-AID., Conclusions: The THA-AID is an automated tool for implant identification from radiographs with exceptional performance on internal and external test sets and no decrement in performance based on radiographic view. Importantly, this is the first study in orthopedics to our knowledge including uncertainty quantification and outlier detection of a DL model., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: To report the clinical outcomes and reoperation rates of arthroscopic and endoscopic iliopsoas release at short-term follow-up after ipsilateral total hip arthroplasty (THA) at 2 separate medical institutions and to evaluate whether demographic and radiographic parameters are associated with postoperative patient-reported outcomes (PROs)., Methods: Patients with iliopsoas tendinitis in the setting of prior THA who underwent arthroscopic iliopsoas fractional lengthening from 1988 to 2023 at 2 academic institutions were reviewed. Patients were included if they had 12 months of follow-up and underwent evaluation of preoperative anterior acetabular component overhang, surgery satisfaction, postoperative subjective hip flexion strength and anterior groin pain improvement, modified Harris Hip Score, Single Assessment Numeric Evaluation score, Tegner activity scale score, visual analog scale (VAS) score, and revision hip arthroplasty., Results: Sixty hips in 58 patients (19 male and 39 female patients) were followed up for a mean of 39.3 months (range, 12.0-105.9 months) postoperatively. Of the patients, 77% reported feeling "much better" or "slightly better," 75% reported improved anterior groin pain, and 60% reported improved subjective hip flexion strength. The surgery satisfaction rating was 7.2 ± 3.3 (scale of 0 to 10). The mean postoperative modified Harris Hip Score, VAS score for pain at rest, VAS score for pain with use, and Single Assessment Numeric Evaluation score were 73.9 ± 19.4, 1.3 ± 2.4, 3.8 ± 2.9, and 71.9 ± 21.9, respectively. Preoperative anterior acetabular component overhang was 3.3 ± 6.5 mm and did not significantly correlate with postoperative PROs ( P ≥ .45). The Tegner score improved from 2.5 ± 1.7 preoperatively to 2.9 ± 1.4 postoperatively ( P  = .0253). Three patients underwent revision arthroplasty at a mean of 25.3 months (range, 11.6-40.4 months) postoperatively, with an acetabular component revision rate of 3.3%., Conclusions: Satisfactory outcomes and low revision arthroplasty rates were observed in patients undergoing arthroscopic iliopsoas lengthening after THA. There was no statistically significant relation between anterior acetabular component overhang and final PROs., Level of Evidence: Level IV, therapeutic case series., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Support was provided by the Foderaro-Quattrone Musculoskeletal-Orthopaedic Surgery Research Innovation Fund. M.J.T. reports board membership with 10.13039/100009885American Academy of Orthopaedic Surgeons, American Association of Hip and Knee Surgeons, Journal of Arthroplasty, Mid-America Orthopaedic Association, Journal of Knee Surgery, Knee Surgery, Sports Traumatology, Arthroscopy, and Orthopedics Today; reports a consulting or advisory relationship with Envois and Onkos. B.A.L. reports a consulting or advisory relationship with 10.13039/100007307Arthrex and owns equity or stocks in COVR Medical. S.J.N. reports board membership with 10.13039/100011549American Orthopaedic Society for Sports Medicine and 10.13039/100008542Arthroscopy Association of North America; receives funding grants from Mitek, Ossur, Springer, and 10.13039/100008894Stryker; and reports a consulting or advisory relationship with Stryker. M.H. reports a consulting or advisory relationship with DJO-Envois, Moximed, and Vericel; receives funding grants from Elsevier; and reports board membership with Journal of Cartilage and Joint Preservation. All other authors (K.N.S., K.J., M.V.) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants)., Methods: The THA-Net is a deep learning algorithm which receives an input preoperative radiograph and subsequently replaces the target hip joint with THA implants to generate a synthetic yet realistic postoperative radiograph. We trained THA-Net on 356,305 pairs of radiographs from 14,357 patients from a single institution's total joint registry and evaluated the validity (quality of surgical execution) and realism (ability to differentiate real and synthetic radiographs) of its outputs against both human-based and software-based criteria., Results: The surgical validity of synthetic postoperative radiographs was significantly higher than their real counterparts (mean difference: 0.8 to 1.1 points on 10-point Likert scale, P < .001), but they were not able to be differentiated in terms of realism in blinded expert review. Synthetic images showed excellent validity and realism when analyzed with already validated deep learning models., Conclusion: We developed a THA next-generation templating tool that can generate synthetic radiographs graded higher on ultimate surgical execution than real radiographs from training data. Further refinement of this tool may potentiate patient-specific surgical planning and enable technologies such as robotics, navigation, and augmented reality (an online demo of THA-Net is available at: https://demo.osail.ai/tha&#95;net)., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Massive modular endoprostheses have become a primary means of reconstruction after oncologic resection of a lower extremity tumor. These implants are commonly made with cobalt-chromium alloys that can undergo wear and corrosion, releasing cobalt and chromium ions into the surrounding tissue and blood. However, there are few studies about the blood metal levels in these patients., Question/purpose: What is the whole blood cobalt and chromium ion level in patients with massive modular endoprostheses?, Methods: We performed a cross-sectional study of our total joints registry to identify patients with a history of an endoprosthetic reconstruction performed at our institution. Patients who were alive at the time of our review in addition to those undergoing an endoprosthetic reconstruction after an oncologic resection were included. Whole blood samples were obtained from 27 (14 male and 13 female) patients with a history of a lower extremity oncologic endoprosthesis. The median time from surgery to blood collection was 8 years (range 6 months to 32 years). Blood samples were collected and stored in metal-free ethylenediaminetetraacetic acid tubes. Samples were analyzed on an inductively coupled plasma mass spectrometer in an International Organization for Standardization seven-class clean room using polytetrafluoroethylene-coated instruments to reduce the risk of metal contamination. The analytical measuring range was 1 to 200 ng/mL for chromium and cobalt. Cobalt and chromium levels were considered elevated when the blood level was ≥ 1 ppb., Results: Cobalt levels were elevated in 59% (16 of 27) of patients, and chromium levels were elevated in 26% (seven of 27). In patients with elevated metal ion values, 15 of 17 patients had a reconstruction using a Stryker/Howmedica Global Modular Replacement System implant., Conclusion: Blood metal levels were elevated in patients who received reconstructions using modular oncology endoprostheses Future work is needed to establish appropriate follow-up routines and determine whether and when systemic complications occur because of elevated metal levels and how to potentially address these elevated levels when complications occur. Prospective and retrospective collaboration between multiple centers and specialty societies will be necessary to address these unknown questions in this potentially vulnerable patient group., Level of Evidence: Level IV, therapeutic study., Competing Interests: Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request., (Copyright © 2023 by the Association of Bone and Joint Surgeons.)

Background: Medical image analysis pipelines often involve segmentation, which requires a large amount of annotated training data, which is time-consuming and costly. To address this issue, we proposed leveraging generative models to achieve few-shot image segmentation., Methods: We trained a denoising diffusion probabilistic model (DDPM) on 480,407 pelvis radiographs to generate 256 ✕ 256 px synthetic images. The DDPM was conditioned on demographic and radiologic characteristics and was rigorously validated by domain experts and objective image quality metrics (Frechet inception distance [FID] and inception score [IS]). For the next step, three landmarks (greater trochanter [GT], lesser trochanter [LT], and obturator foramen [OF]) were annotated on 45 real-patient radiographs; 25 for training and 20 for testing. To extract features, each image was passed through the pre-trained DDPM at three timesteps and for each pass, features from specific blocks were extracted. The features were concatenated with the real image to form an image with 4225 channels. The feature-set was broken into random patches, which were fed to a U-Net. Dice Similarity Coefficient (DSC) was used to compare the performance with a vanilla U-Net trained on radiographs., Results: Expert accuracy was 57.5 % in determining real versus generated images, while the model reached an FID = 7.2 and IS = 210. The segmentation UNet trained on the 20 feature-sets achieved a DSC of 0.90, 0.84, and 0.61 for OF, GT, and LT segmentation, respectively, which was at least 0.30 points higher than the naively trained model., Conclusion: We demonstrated the applicability of DDPMs as feature extractors, facilitating medical image segmentation with few annotated samples., Competing Interests: Declaration of Competing Interest The authors have no actual or potential conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Implementing tools that identify cost-saving opportunities for ambulatory orthopaedic surgeries can improve access to value-based care. We developed and internally validated a machine learning (ML) algorithm to predict cost drivers of total charges after ambulatory unicompartmental knee arthroplasty (UKA)., Methods: We queried the New York State Ambulatory Surgery and Services database to identify patients who underwent ambulatory, defined as <24 hours of care before discharge, elective UKA between 2014 and 2016. A total of 1,311 patients were included. The median costs after ambulatory UKA were $14,710. Patient demographics and intraoperative parameters were entered into 4 candidate ML algorithms. The most predictive model was selected following internal validation of candidate models, with conventional linear regression as a benchmark. Global variable importance and partial dependence curves were constructed to determine the impact of each input parameter on total charges., Results: The gradient-boosted ensemble model outperformed all candidate algorithms and conventional linear regression. The major differential cost drivers of UKA identified (in decreasing order of magnitude) were increased operating room time, length of stay, use of regional and adjunctive periarticular analgesia, utilization of computer-assisted navigation, and routinely sending resected tissue to pathology., Conclusion: We developed and internally validated a supervised ML algorithm that identified operating room time, length of stay, use of computer-assisted navigation, regional primary anesthesia, adjunct periarticular analgesia, and routine surgical pathology as essential cost drivers of UKA. Following external validation, this tool may enable surgeons and health insurance providers optimize the delivery of value-based care to patients receiving outpatient UKA., Level of Evidence: III., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients' data and for classification and predictive purposes. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electronic health records only accessible by manual extraction, which takes extensive time and resources. Natural language processing (NLP), a field within artificial intelligence, may offer a viable alternative to manual extraction. Using NLP, a researcher can analyze written and spoken data and extract data in an organized manner suitable for future research and clinical use. This article will first discuss common subtasks involved in an NLP pipeline, including data preparation, modeling, analysis, and external validation, followed by examples of NLP projects. Challenges and limitations of NLP will be discussed, closing with future directions of NLP projects, including large language models., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: In this work, we applied and validated an artificial intelligence technique known as generative adversarial networks (GANs) to create large volumes of high-fidelity synthetic anteroposterior (AP) pelvis radiographs that can enable deep learning (DL)-based image analyses, while ensuring patient privacy., Methods: AP pelvis radiographs with native hips were gathered from an institutional registry between 1998 and 2018. The data was used to train a model to create 512 × 512 pixel synthetic AP pelvis images. The network was trained on 25 million images produced through augmentation. A set of 100 random images (50/50 real/synthetic) was evaluated by 3 orthopaedic surgeons and 2 radiologists to discern real versus synthetic images. Two models (joint localization and segmentation) were trained using synthetic images and tested on real images., Results: The final model was trained on 37,640 real radiographs (16,782 patients). In a computer assessment of image fidelity, the final model achieved an "excellent" rating. In a blinded review of paired images (1 real, 1 synthetic), orthopaedic surgeon reviewers were unable to correctly identify which image was synthetic (accuracy = 55%, Kappa = 0.11), highlighting synthetic image fidelity. The synthetic and real images showed equivalent performance when they were assessed by established DL models., Conclusion: This work shows the ability to use a DL technique to generate a large volume of high-fidelity synthetic pelvis images not discernible from real imaging by computers or experts. These images can be used for cross-institutional sharing and model pretraining, further advancing the performance of DL models without risk to patient data safety., Level of Evidence: Level III., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single deep learning model to annotate certain anatomical structures and landmarks on antero-posterior (AP) pelvis radiographs., Methods: A total of 1,100 AP pelvis radiographs were manually annotated by 3 reviewers. These images included a mix of preoperative and postoperative images as well as a mix of AP pelvis and hip images. A convolutional neural network was trained to segment 22 different structures (7 points, 6 lines, and 9 shapes). Dice score, which measures overlap between model output and ground truth, was calculated for the shapes and lines structures. Euclidean distance error was calculated for point structures., Results: Dice score averaged across all images in the test set was 0.88 and 0.80 for the shape and line structures, respectively. For the 7-point structures, average distance between real and automated annotations ranged from 1.9 mm to 5.6 mm, with all averages falling below 3.1 mm except for the structure labeling the center of the sacrococcygeal junction, where performance was low for both human and machine-produced labels. Blinded qualitative evaluation of human and machine produced segmentations did not reveal any drastic decrease in performance of the automatic method., Conclusion: We present a deep learning model for automated annotation of pelvis radiographs that flexibly handles a variety of views, contrasts, and operative statuses for 22 structures and landmarks., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Identifying ambulatory surgical candidates at risk for adverse surgical outcomes can optimize outcomes. The purpose of this study was to develop and internally validate a machine learning (ML) algorithm to predict contributors to unexpected hospitalizations after ambulatory unicompartmental knee arthroplasty (UKA)., Methods: A total of 2,521 patients undergoing UKA from 2006 to 2018 were retrospectively evaluated. Patients admitted overnight postoperatively were identified as those who had a length of stay ≥ 1 day were analyzed with four individual ML models (ie, random forest, extreme gradient boosting, adaptive boosting, and elastic net penalized logistic regression). An additional model was produced as a weighted ensemble of the four individual algorithms. Area under the receiver operating characteristics (AUROC) compared predictive capacity of these models to conventional logistic regression techniques., Results: Of the 2,521 patients identified, 103 (4.1%) required at least one overnight stay following ambulatory UKA. The ML ensemble model achieved the best performance based on discrimination assessed via internal validation (AUROC = 87.3), outperforming individual models and conventional logistic regression (AUROC = 81.9-85.7). The variables determined most important by the ensemble model were cumulative time in the operating room, utilization of general anesthesia, increasing age, and patient residency in more urban areas. The model was integrated into a web-based open-access application., Conclusion: The ensemble gradient-boosted ML algorithm demonstrated the highest performance in identifying factors contributing to unexpected hospitalizations in patients receiving UKA. This tool allows physicians and healthcare systems to identify patients at a higher risk of needing inpatient care after UKA., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Studies developing predictive models from large datasets to risk-stratify patients under going revision total hip arthroplasties (rTHAs) are limited. We used machine learning (ML) to stratify patients undergoing rTHA into risk-based subgroups., Methods: We retrospectively identified 7,425 patients who underwent rTHA from a national database. An unsupervised random forest algorithm was used to partition patients into high-risk and low-risk strata based on similarities in rates of mortality, reoperation, and 25 other postoperative complications. A risk calculator was produced using a supervised ML algorithm to identify high-risk patients based on preoperative parameters., Results: There were 3,135 and 4,290 patients identified in the high-risk and low-risk subgroups, respectively. Each group significantly differed by rate of 30-day mortalities, unplanned reoperations/readmissions, routine discharges, and hospital lengths of stay (P < .05). An Extreme Gradient Boosting algorithm identified preoperative platelets < 200, hematocrit > 35 or < 20, increasing age, albumin < 3, international normalized ratio > 2, body mass index > 35, American Society of Anesthesia class ≥ 3, blood urea nitrogen > 50 or < 30, creatinine > 1.5, diagnosis of hypertension or coagulopathy, and revision for periprosthetic fracture and infection as predictors of high risk., Conclusion: Clinically meaningful risk strata in patients undergoing rTHA were identified using an ML clustering approach. Preoperative labs, demographics, and surgical indications have the greatest impact on differentiating high versus low risk., Level of Evidence: III., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in popularity as a discipline for better understanding visual data. Computer Vision has become a powerful tool for imaging analytics in orthopedic surgery, allowing computers to evaluate large volumes of image data with greater nuance than previously possible. Nevertheless, even with the growing number of uses in medicine, literature on the fundamentals of CV and its implementation is mainly oriented toward computer scientists rather than clinicians, rendering CV unapproachable for most orthopedic surgeons as a tool for clinical practice and research. The purpose of this article is to summarize and review the fundamental concepts of CV application for the orthopedic surgeon and musculoskeletal researcher., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Radiographic markers contain protected health information that must be removed before public release. This work presents a deep learning algorithm that localizes radiographic markers and selectively removes them to enable de-identified data sharing. The authors annotated 2000 hip and pelvic radiographs to train an object detection computer vision model. Data were split into training, validation, and test sets at the patient level. Extracted markers were then characterized using an image processing algorithm, and potentially useful markers (eg, "L" and "R") without identifying information were retained. The model achieved an area under the precision-recall curve of 0.96 on the internal test set. The de-identification accuracy was 100% (400 of 400), with a de-identification false-positive rate of 1% (eight of 632) and a retention accuracy of 93% (359 of 386) for laterality markers. The algorithm was further validated on an external dataset of chest radiographs, achieving a de-identification accuracy of 96% (221 of 231). After fine-tuning the model on 20 images from the external dataset to investigate the potential for improvement, a 99.6% (230 of 231, P = .04) de-identification accuracy and decreased false-positive rate of 5% (26 of 512) were achieved. These results demonstrate the effectiveness of a two-pass approach in image de-identification. Keywords: Conventional Radiography, Skeletal-Axial, Thorax, Experimental Investigations, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Chang and Li in this issue., Competing Interests: Disclosures of conflicts of interest: B.K. Member of the Radiology: Artificial Intelligence trainee editorial board. J.P.M. No relevant relationships. P.R. Member of RadioGraphics TEAM. M.J.T. No relevant relationships. A.N.L. Royalties from Globus to Mayo Orthopedics and to author; consulting fees from Globus, Orthopediatrics, Stryker, Depuy Synthes, Alexion, ZimVie, and Medtronic (all funds directed to Mayo orthopedic research); patent to Mayo on vertebral body tethering; participation on Medtronic Advisory Board and Steering Committee for Braive study; board member at POSNA and the Setting Scoliosis Straight Foundation; committee member at the Scoliosis Research Society; leadership or fiduciary role on the research council at the Pediatric Spine Study Group. B.J.E. Research chair for SIIM; stock/stock options in FlowSIGMA, VoiceIT, and Yunu; consultant to the editor for Radiology: Artificial Intelligence. C.C.W. No relevant relationships., (© 2023 by the Radiological Society of North America, Inc.)

Background: The hip-spine relationship is increasingly recognized as critical for optimizing stability following total hip arthroplasty (THA). However, these measurements are not routinely obtained during THA workup. It has been suggested that insight can be gained from supine antero-posterior pelvis radiograph, measuring the distance from the superior border of the pubic symphysis to the sacro-coccygeal joint (PSCD). This study assessed the correlation between PSCD and lateral lumbar radiographic metrics in a cohort of preoperative THA patients., Methods: We retrospectively evaluated 250 consecutive patients who underwent THA with preoperative supine antero-posterior pelvis and lateral lumbar radiographs. The mean age was 68 years (range, 42 to 89), 61% were women, and the mean body mass index was 30 kg/m 2 (range, 19 to 52). Two reviewers measured PSCD, pelvic tilt (PT), sacral slope (SS), pelvic incidence (PI), and lumbar lordosis (LL). Inter-observer reliability was calculated for all measurements, and correlation coefficients were calculated for PSCD with respect to PT, SS, PI, and LL., Results: Correlations between PSCD and lumbar radiographic metrics were all statistically significant, except for PI in men but graded as "weak" or "very weak" for men and women, respectively, as follows: PT = -0.30 (P < .01) and -0.46 (P < .01); SS = 0.27 (P < .01) and 0.22 (P < .01); PI = -0.04 (P = .70) and -0.19 (P = .02); and LL = 0.45 (P < .01) and 0.30 (P < .01). Inter-observer reliability was graded as "strong" for every metric., Conclusion: The PSCD was weakly correlated with all evaluated lateral lumbar radiographic metrics in both sexes, despite strong inter-observer reliability. Therefore, PSCD cannot reliably serve as a proxy for evaluating the hip-spine relationship., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Dual-mobility (DM) implants reduce the risk of dislocation in patients who have undergone total hip arthroplasty (THA); however, DM implants are at risk for large-head dislocation and intraprosthetic dissociation (IPD), where the inner femoral head dissociates from the outer polyethylene head. This study aimed to report the incidence of DM dislocation and IPD, evaluate the rate of recognition of IPD before and after reduction, investigate the outcomes of these complications, and provide treatment recommendations for their management., Methods: Between 2010 and 2021, 695 primary and 758 revision THAs were performed with DM constructs at a single institution. There were 44 large-head dislocations (3.0%) and 10 IPDs (0.7%). Four additional IPDs occurred during attempted closed reduction, increasing the IPD incidence to 0.96%. We reviewed prior instability history, dislocation management, success of reduction, recognition of IPD, and subsequent rates of revision and complications. The mean follow-up was 2.5 years., Results: Nine of 10 IPDs were missed at presentation and thus not treated as such. Sixty-three percent of attempted closed reductions in the emergency department failed and led to 4 IPDs and 1 periprosthetic fracture. Reduction success was associated with the following factors: use of general anesthesia with paralysis (p = 0.02), having the reduction performed by an orthopaedist (p = 0.03), and undergoing only 1 reduction attempt (p = 0.015). Two-thirds of dislocations required revision. The rate of redislocation was 33%, and 5 hips required subsequent revision at a mean of 1.8 years after the initial dislocation., Conclusions: We present an evaluation of DM-implant dislocation and dissociation along with management recommendations based on these data. Given the low success and high complication rates of attempted closed reduction and the need for eventual revision, we recommend that all patients with dislocated DM implants be brought to the operating room for closed reduction as well as potential revision if the reduction fails., Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence., (Copyright © 2023 The Authors. Published by The Journal of Bone and Joint Surgery, Incorporated. All rights reserved.)

Background: Direct anterior approach (DAA) total hip arthroplasty (THA) has been associated with higher rates of superficial incisional dehiscence. However, limited data are available regarding the outcomes following initial treatment of this complication. This study aimed to evaluate patient risk factors, reoperations, and revisions in those who developed superficial wound dehiscence following DAA THA., Methods: We identified 3,687 patients who underwent a primary DAA THA between 2010 and 2019 from our enterprise total joint registry. Of these, 98 (2.7%) patients developed a superficial wound dehiscence requiring intervention [irrigation and debridement (n = 42) or wound care with or without antibiotics (n = 56)]. Dehiscence was noted at a median of 27 (range, 2-105) days. These patients were compared to patients who did not have a superficial wound complication (n = 3,589). Landmark survivorship analysis was performed to account for immortal time bias with a 45-day landmark time., Results: Patients who had superficial wound dehiscence compared to those who did not, were more often women (64 versus 53%, P = .02) and had increased mean body mass index (33 versus 29, P < .001). There was no difference in 4-year survivorship free from any revision between cohorts (97 versus 98%, respectively, P = .14). There were 2 (2.0%) revisions in the superficial dehiscence group: 1 for periprosthetic joint infection and 1 for aseptic femoral loosening., Conclusion: Superficial wound dehiscence following DAA THA was associated with higher body mass index and was more common in women. Fortunately, with proper index management, the risk of revision THA and periprosthetic joint infection was not increased for these patients., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Spinal anesthesia is increasingly used in complex patient populations including revision total hip arthroplasties (THAs). This study aimed to investigate the pain control, length of stay (LOS), and complications associated with spinal versus general anesthesia in a large institutional series of revision THAs., Methods: We retrospectively identified 4,767 revision THAs (4,533 patients) from 2001 to 2016 using our institutional total joint registry. Of these cases, 86% had general and 14% had spinal anesthesia. Demographics between groups were similar with mean age of 66 years, 52% women, and mean body mass index of 29. Complications including all-cause rerevisions and reoperations were studied. Data were analyzed using an inverse probability of treatment weighted model based on propensity score that accounted for patient and surgical factors. The mean follow-up was 7 years., Results: Patients treated with spinal anesthesia required fewer postoperative oral morphine equivalents (P < .001) and had lower numeric pain rating scale scores (P < .001). Spinal anesthesia had a decreased LOS (4.2 versus 4.8 days; P = .007), fewer cases of altered mental status (odds ratio (OR) 3.1, P = .001), fewer blood transfusions (OR 2.3, P < .001), fewer intensive care unit admissions (OR 2.3, P < .001), fewer rerevisions (OR 1.6, P = .04), and fewer reoperations (OR 1.5, P = .02)., Conclusion: Spinal anesthesia was associated with lower oral morphine equivalent use and reduced LOS in this large cohort of revision THAs. Furthermore, spinal anesthesia was associated with fewer cases of altered mental status, transfusion, intensive care unit admission, rerevision, and reoperation after accounting for numerous patient and operative factors., Level of Evidence: Level III, Retrospective Comparative Study., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Many risk factors have been described for periprosthetic femur fracture (PPFFx) following total hip arthroplasty (THA), yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to develop a high-dimensional, patient-specific risk-stratification nomogram that allows dynamic risk modification based on operative decisions., Methods: We evaluated 16,696 primary nononcologic THAs performed between 1998 and 2018. During a mean 6-year follow-up, 558 patients (3.3%) sustained a PPFFx. Patients were characterized by individual natural language processing-assisted chart review on nonmodifiable factors (demographics, THA indication, and comorbidities), and modifiable operative decisions (femoral fixation [cemented/uncemented], surgical approach [direct anterior, lateral, and posterior], and implant type [collared/collarless]). Multivariable Cox regression models and nomograms were developed with PPFFx as a binary outcome at 90 days, 1 year, and 5 years, postoperatively., Results: Patient-specific PPFFx risk based on comorbid profile was wide-ranging from 0.4-18% at 90 days, 0.4%-20% at 1 year, and 0.5%-25% at 5 years. Among 18 evaluated patient factors, 7 were retained in multivariable analyses. The 4 significant nonmodifiable factors included the following: women (hazard ratio (HR) = 1.6), older age (HR = 1.2 per 10 years), diagnosis of osteoporosis or use of osteoporosis medications (HR = 1.7), and indication for surgery other than osteoarthritis (HR = 2.2 for fracture, HR = 1.8 for inflammatory arthritis, HR = 1.7 for osteonecrosis). The 3 modifiable surgical factors were included as follows: uncemented femoral fixation (HR = 2.5), collarless femoral implants (HR = 1.3), and surgical approach other than direct anterior (lateral HR = 2.9, posterior HR = 1.9)., Conclusion: This patient-specific PPFFx risk calculator demonstrated a wide-ranging risk based on comorbid profile and enables surgeons to quantify risk mitigation based on operative decisions., Level of Evidence: Level III, Prognostic., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The advent of highly porous ingrowth surfaces and highly crosslinked polyethylene has been expected to improve implant survivorship in revision total hip arthroplasty. Therefore, we sought to evaluate the survival of several contemporary acetabular designs following revision total hip arthroplasty., Methods: Acetabular revisions performed from 2000 to 2019 were identified from our institutional total joint registry. We studied 3,348 revision hips, implanted with 1 of 7 cementless acetabular designs. These were paired with highly crosslinked polyethylene or dual-mobility liners. A historical series of 258 Harris-Galante-1 components, paired with conventional polyethylene, was used as reference. Survivorship analyses were performed. For the 2,976 hips with minimum 2-year follow-up, the median follow-up was 8 years (range, 2 to 35 years)., Results: Contemporary components with adequate follow-up had survivorship free of acetabular rerevision of ≥95% at 10-year follow-up. Relative to Harris-Galante-1 components, 10-year survivorship free of all-cause acetabular cup rerevision was significantly higher in Zimmer Trabecular Metarevision (hazard ratio (HR) 0.3, 95% confidence interval (CI) 0.2-0.45), Zimmer Trabecular MetaModular (HR 0.34, 95% CI 0.13-0.89), Zimmer Trilogy (HR 0.4, 95% CI 0.24-0.69), DePuy Pinnacle Porocoat (HR 0.24, 95% CI 0.11-0.51), and Stryker Tritanium revision (HR 0.46, 95% CI 0.24-0.91) shells. Among contemporary components, there were only 23 rerevisions for acetabular aseptic loosening and no rerevisions for polyethylene wear., Conclusion: Contemporary acetabular ingrowth and bearing surfaces were associated with no rerevisions for wear and aseptic loosening was uncommon, particularly with highly porous designs. Therefore, it appears that contemporary revision acetabular components have dramatically improved upon historical results at available follow-up., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The role of medications to prevent arthrofibrosis following total knee arthroplasty (TKA) remains unclear. We investigated the effect of common oral medications with reported antifibrotic properties on preventing arthrofibrosis and manipulation under anesthesia (MUA) following primary TKA., Methods: Using our total joint registry, 9,771 patients (12,735 knees) who underwent TKA with cemented, posterior-stabilized, and metal-backed tibial components from 2000 to 2016 were identified. Arthrofibrosis, defined as range of motion (ROM) ≤90° for ≥12 weeks postoperatively or as ROM ≤90° requiring MUA, was diagnosed in 454 knees (4%) and matched 1:2 to controls. Mean age was 62 years (range, 19 to 87) and 57% were women. The majority of operative diagnoses were osteoarthritis. Perioperative use of 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase inhibitors (statins), angiotensin converting enzyme inhibitors (ACE inhibitors), angiotensin II receptor blockers (ARBs), oral corticosteroids, antihistamines, and nonsteroidal anti-inflammatory drugs (NSAIDs) were manually confirmed. Medication effect in preventing arthrofibrosis and MUA was assessed using adjusted multivariable analyses. Mean follow-up was 8 years (range, 2 to 20)., Results: Reduced risk of arthrofibrosis was associated with perioperative NSAID use (odds ratio (OR) 0.67, P = .045). A similar trend was observed with perioperative corticosteroids (OR 0.52, P = .098). Corticosteroids were associated with reduced risk of MUA (OR 0.26, P = .036), and NSAIDs trended towards reducing MUA (OR 0.69, P = .11)., Conclusion: This investigation determined that perioperative NSAID use was associated with reduced risk of arthrofibrosis and trended towards reduced risk of subsequent MUA. Similarly, oral corticosteroids were associated with reduced risk of MUA and trended towards reduced risk of arthrofibrosis., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Aims: Knowledge on total knee arthroplasties (TKAs) in patients with a history of poliomyelitis is limited. This study compared implant survivorship and clinical outcomes among affected and unaffected limbs in patients with sequelae of poliomyelitis undergoing TKAs., Methods: A retrospective review of our total joint registry identified 94 patients with post-polio syndrome undergoing 116 primary TKAs between January 2000 and December 2019. The mean age was 70 years (33 to 86) with 56% males (n = 65) and a mean BMI of 31 kg/m 2 (18 to 49). Rotating hinge TKAs were used in 14 of 63 affected limbs (22%), but not in any of the 53 unaffected limbs. Kaplan-Meier survivorship analyses were completed. The mean follow-up was eight years (2 to 19)., Results: The ten-year survivorship free from revision was 91% (95% confidence interval (CI) 81 to 100) in affected and 84% (95% CI 68 to 100) in unaffected limbs. There were six revisions in affected limbs: three for periprosthetic femoral fractures and one each for periprosthetic joint infection (PJI), patellar clunk syndrome, and instability. Unaffected limbs were revised in four cases: two for instability and one each for PJI and tibial component loosening. The ten-year survivorship free from any reoperation was 86% (95% CI 75 to 97) and 80% (95% CI 64 to 99) in affected and unaffected limbs, respectively. There were three additional reoperations among affected and two in unaffected limbs. There were 12 nonoperative complications, including four periprosthetic fractures. Arthrofibrosis occurred in five affected (8%) and two unaffected limbs (4%). Postoperative range of motion decreased with 31% achieving less than 90° knee flexion by five years., Conclusion: TKAs in post-polio patients are complex cases associated with instability, and one in four require constraint on the affected side. Periprosthetic fracture was the main mode of failure. Arthrofibrosis rates were high and twice as frequent in affected limbs., Competing Interests: M. J. Taunton reports consulting fees from ONKOS and Enovis, unrelated to this study. D. G. Lewallen reports royalties or licenses from Zimmer Biomet, consulting fees from Accuitive Technologies and Zimmer Biomet, stock or stock options in Accuitive Technologies and Ketai Medical Devices, and research support from Corin, all of which are unrelated to this study. M. P. Abdel reports royalties or licenses from Stryker, OsteoRemedies, and Springer, unrelated to this study., (© 2023 The British Editorial Society of Bone & Joint Surgery.)

Background: Interest in spinal anesthesia utilization in revision total knee arthroplasties (TKAs) is rising. This study investigated the pain control, length of stay (LOS), and complications associated with spinal versus general anesthesia in a single institution series of revision TKAs., Methods: We identified 3,711 revision TKAs (3,495 patients) from 2001 to 2016 using our institutional total joint registry. There were 66% who had general anesthesia and 34% who had spinal anesthesia. Mean age, sex, and BMI were similar between groups at 67 years, 53% women, and 32, respectively. Data were analyzed using inverse probability of treatment weighted models based on propensity scores that accounted for patient and operative factors. Mean follow-up was 6 years (range, 2 to 17)., Results: Patients treated with spinal anesthesia required fewer postoperative oral morphine equivalents (OMEs) (P < .0001) and had lower numeric pain rating scale scores (P < .001). Spinal anesthesia was associated with shorter LOS (4.0 versus 4.6 days; P < .0001), less cases of altered mental status (AMS; Odds Ratio (OR) 2.0, P = .004), less intensive care unit (ICU) admissions (OR 1.6, P = .02), fewer re-revisions (OR 1.7, P < .001), and less reoperations (OR 1.4, P < .001). There was no difference in the incidence of VTE (P = .82), 30-day readmissions (P = .06), or 90-day readmissions (P = .18) between anesthetic techniques., Conclusion: We found that spinal anesthesia for revision TKAs was associated with significantly lower pain scores, reduced OME requirements, and decreased LOS. Furthermore, spinal anesthesia was associated with fewer cases of AMS, ICU admissions, and re-revisions even after accounting for numerous patient and operative factors., Level of Evidence: Level III, Retrospective Comparative Study., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Perioperative medical management during total hip arthroplasty (THA) is continuously improving, allowing an increasing number of medically complex patients to undergo total joint arthroplasty. This study examined mortalities, medical complications, implant survivorships, and clinical outcomes of THA in patients who have pulmonary hypertension (HTN)., Methods: We identified 638 patients who had pulmonary HTN and underwent 508 primary THAs and 191 revision THAs from 2000 to 2016 at a tertiary care center. Patients were followed up at regular intervals until death, revision surgery, or last clinical follow-up. Perioperative medical complications were individually reviewed. The risk of death was examined by calculating standardized mortality ratios and Cox proportional hazards regression models. Cumulative incidence analyses were used for reporting mortality, reoperation, and revision with death as a competing risk., Results: The 90-day mortality was 1.8% and 3.1% for primary and revision THAs, respectively. The risk of death was approximately two-fold higher compared to primary (hazard ratio 2.69) and revision (hazard ratio 2.04) THA patients who did not have pulmonary HTN. Rate of medical complications within 90 days from surgery were 6.2% and 13.1% in primary and revision THAs, respectively. The 10-year cumulative incidence of any revision was 9% and 14% following primaries and revisions, respectively., Conclusion: Patients who had pulmonary HTN undergoing primary and revision THAs had an increased risk of death and experienced a high rate of medical complications within 90 days of surgery. Counseling of risks, medical optimization, and referral to medical centers expert at managing complex medical problems should be considered., Level of Evidence: Level IV., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Failed stemmed total knee arthroplasty (TKA) components present with varying degrees of bone loss and technical challenges. A classification system has been proposed based upon metaphyseal bone loss and diaphyseal cortical integrity. A validation study was performed to determine interobserver and intraobserver reliability at multiple institutions and with different levels of training., Methods: An online survey with digital anteroposterior and lateral radiographs was sent to 5 arthroplasty surgeons and 5 adult reconstruction fellows. The survey included 62 cases with stemmed femoral and tibial components, considered failures and pending revision, and scored by each reviewer independently using the classification system. Each case was scored in 2 separate sessions. Interobserver and intraobserver reliability was assessed using the intraclass correlation coefficient (ICC)., Results: Interobserver grading for both the femur (0.69) and tibia (0.72) showed strong reliability among the attendings and fellows, with slightly stronger reliability in tibia cases. The intraclass correlation coefficient (ICC) for attendings and fellows was similar overall, demonstrating consistency of the grading regardless of training level. Intraobserver comparisons showed a strong ICC for attendings and fellows in femoral cases, while fellows had near-perfect ICC in tibia cases. Across all reviewers there was on average 93% agreement within 1 grade per case with the majority of the discrepancy occurring at the metaphyseal-diaphyseal junction., Conclusion: This classification demonstrated overall strong interobserver and intraobserver reliability, with 93% agreement within 1 grade of bone loss. With further education, this classification system can ultimately be used to standardize the degree of bone loss in failed stemmed components and help with preoperative planning., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Knee osteoarthritis (OA) is a prevalent joint disease. Clinical prediction models consider a wide range of risk factors for knee OA. This review aimed to evaluate published prediction models for knee OA and identify opportunities for future model development., Methods: We searched Scopus, PubMed, and Google Scholar using the terms knee osteoarthritis, prediction model, deep learning, and machine learning. All the identified articles were reviewed by one of the researchers and we recorded information on methodological characteristics and findings. We only included articles that were published after 2000 and reported a knee OA incidence or progression prediction model., Results: We identified 26 models of which 16 employed traditional regression-based models and 10 machine learning (ML) models. Four traditional and five ML models relied on data from the Osteoarthritis Initiative. There was significant variation in the number and type of risk factors. The median sample size for traditional and ML models was 780 and 295, respectively. The reported Area Under the Curve (AUC) ranged between 0.6 and 1.0. Regarding external validation, 6 of the 16 traditional models and only 1 of the 10 ML models validated their results in an external data set., Conclusion: Diverse use of knee OA risk factors, small, non-representative cohorts, and use of magnetic resonance imaging which is not a routine evaluation tool of knee OA in daily clinical practice are some of the main limitations of current knee OA prediction models.

Aims: Spinal anaesthesia has seen increased use in contemporary primary total knee arthroplasties (TKAs). However, controversy exists about the benefits of spinal in comparison to general anaesthesia in primary TKAs. This study aimed to investigate the pain control, length of stay (LOS), and complications associated with spinal versus general anaesthesia in primary TKAs from a single, high-volume academic centre., Methods: We retrospectively identified 17,690 primary TKAs (13,297 patients) from 2001 to 2016 using our institutional total joint registry, where 52% had general anaesthesia and 48% had spinal anaesthesia. Baseline characteristics were similar between cohorts with a mean age of 68 years (SD 10), 58% female (n = 7,669), and mean BMI of 32 kg/m 2 (SD 7). Pain was evaluated using oral morphine equivalents (OMEs) and numerical pain rating scale (NPRS) data. Complications including 30- and 90-day readmissions were studied. Data were analyzed using an inverse probability of treatment weighted model based on propensity score that included many patient and surgical factors. Mean follow-up was seven years (2 to 18)., Results: Patients treated with spinal anaesthesia required fewer postoperative OMEs (p < 0.001) and had lower NPRS scores (p < 0.001). Spinal anaesthesia also had fewer cases of altered mental status (AMS; odds ratio (OR) 1.3; p = 0.044), as well as 30-day (OR 1.4; p < 0.001) and 90-day readmissions (OR 1.5; p < 0.001). General anaesthesia was associated with increased risk of any revision (OR 1.2; p = 0.021) and any reoperation (1.3; p < 0.001)., Conclusion: In the largest single institutional report to date, we found that spinal anaesthesia was associated with significantly lower OME use, lower risk of AMS, and lower overall 30- and 90-day readmissions following primary TKAs. Additionally, spinal anaesthesia was associated with reduced risk of any revision and any reoperation after accounting for numerous patient and operative factors. When possible and safe, spinal anaesthesia should be considered in primary TKAs.Cite this article: Bone Joint J  2022;104-B(11):1209-1214.