Can machine learning models predict failure of revision total hip arthroplasty?

INTRODUCTION: Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty. METHODS: A total of 2588 consecutive patients that underwent revision THA was evaluated, including 408 patients (15.7%) with confirmed re-revision THA. Electronic patient records were manually reviewed to identify patient demographics, implant characteristics and surgical variables that may be associated with re-revision THA. Machine learning algorithms were developed to predict re-revision THA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for re-revision THA as predicted by the four validated machine learning models were the American Society of Anaesthesiology score, obesity (> 35 kg/m2) and indication for revision THA. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.80), calibration and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: This study developed four machine learning models for the prediction of re-revision surgery for patients following revision total hip arthroplasty. The study findings show excellent model performance, highlighting the potential of these computational models to assist in preoperative patient optimization and counselling to improve revision THA patient outcomes. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

The utility of machine learning algorithms for the prediction of patient-reported outcome measures following primary hip and knee total joint arthroplasty.

BACKGROUND: Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty. METHODS: A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis. RESULTS: The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis. CONCLUSION: This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty. LEVEL OF EVIDENCE: Level III, case control retrospective analysis.

Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models.

BACKGROUND: Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty. METHODS: A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN). RESULTS: We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m2) were the strongest predictors associated with surgical operative time. CONCLUSIONS: This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time. LEVEL OF EVIDENCE: Level III, case control retrospective analysis.

Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty.

PURPOSE: Adequate postoperative pain control following total knee arthroplasty (TKA) is required to achieve optimal patient recovery. However, the postoperative recovery may lead to an unnaturally extended opioid use, which has been associated with adverse outcomes. This study hypothesizes that machine learning models can accurately predict extended opioid use following primary TKA. METHODS: A total of 8873 consecutive patients that underwent primary TKA were evaluated, including 643 patients (7.2%) with extended postoperative opioid use (> 90 days). Electronic patient records were manually reviewed to identify patient demographics and surgical variables associated with prolonged postoperative opioid use. Five machine learning algorithms were developed, encompassing the breadth of state-of-the-art machine learning algorithms available in the literature, to predict extended opioid use following primary TKA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The strongest predictors for prolonged opioid prescription following primary TKA were preoperative opioid duration (100% importance; p < 0.01), drug abuse (54% importance; p < 0.01), and depression (47% importance; p < 0.01). The five machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration, and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: The study findings show excellent model performance for the prediction of extended postoperative opioid use following primary total knee arthroplasty, highlighting the potential of these models to assist in preoperatively identifying at risk patients, and allowing the implementation of individualized peri-operative counselling and pain management strategies to mitigate complications associated with prolonged opioid use. LEVEL OF EVIDENCE: IV.

Machine learning models accurately predict recurrent infection following revision total knee arthroplasty for periprosthetic joint infection.

PURPOSE: This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection. METHODS: A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81-0.84). CONCLUSION: This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes. LEVEL OF EVIDENCE: IV.

Sensitivity and Specificity of Serum and Synovial Fluid Markers in Diagnosis of Infection in Head-Neck Taper Corrosion of Metal-On-Polyethylene Total Hip Arthroplasty.

BACKGROUND: The accurate diagnosis of periprosthetic joint infection (PJI) in the setting of adverse local tissue reactions in patients with metal-on-polyethylene (MoP) total hip arthroplasty (THA) secondary to head-neck taper junction corrosion is challenging as it frequently has the appearance of purulence. The aim of this study is to evaluate the utility of erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and synovial fluid markers in diagnosing PJI in failed MoP THA due to head-neck taper corrosion. METHODS: A total of 89 consecutive patients with MoP THA with head-neck taper corrosion in 2 groups was evaluated: (1) infection group (n = 11) and (2) noninfection group (n = 78). All patients had highly crossed polyethylene with cobalt chromium femoral heads and had preoperative synovial fluid aspiration. In addition, serum cobalt and chromium levels were analyzed. RESULTS: The optimal cutoff value for synovial white blood cell was 2144 with 93% sensitivity and 84% specificity. Neutrophil count optimal cutoff value was 82% with 93% sensitivity and 82% specificity. Receiver operating characteristic analysis of ESR and CRP determined optimal cutoff at 57 mm/h and 35 mg/L with 57% sensitivity and 94% specificity and 93% sensitivity and 76% specificity, respectively. There were no significant differences in metal ion levels between the infected and noninfected groups. CONCLUSION: The results of this study suggest that ESR and CRP are useful in excluding PJI, whereas both synovial white blood cell count and neutrophil percentage in hip aspirate are useful markers for diagnosing infection in MoP THA patients with head-neck taper corrosion associated adverse local tissue reaction.

MARS MRI Characteristics of Adverse Local Tissue Reactions in Taper Corrosion of Metal-On-Polyethylene THA Differ From Metal-On-Metal THA.

BACKGROUND: Metal artifact reduction sequence (MARS) magnetic resonance imaging (MRI) has been recommended as a cross-sectional imaging modality in clinical evaluation of adverse local tissue reactions (ALTRs) in metal-on-metal (MoM) patients and metal-on-polyethylene (MoP) patients with taper corrosion. The aim of the study was to compare MARS MRI characteristics of ALTR in MoM total hip arthroplasty (THA) with ALTR in MoP THA with modular taper corrosion. METHODS: A total of 197 patients with ALTR were evaluated: 86 patients with MoM THA; 37 MoP patients with head-neck taper corrosion; and 74 MoP patients with neck-stem dual taper corrosion. MARS MRI scans were evaluated to identify location, size, type of ALTR (I-III), and associated ALTR synovitis (cystic, solid, and mixed). RESULTS: MARS MRI characteristics of ALTR were significantly different between the MoM and MoP groups (P = .017). The MoP group demonstrated the highest proportion of thick-walled cystic masses type II (56.7% in head-neck taper corrosion MoP and 46.5% in dual taper corrosion MoP vs 28.7% in MoM), whereas the MoM group had the highest proportion of thin-walled cystic masses type I. MoM implants (96.8%) were significantly more likely to have ALTR in multiple locations compared with both MoP groups (P = .001). CONCLUSION: This study demonstrates that MARS MRI characteristics of ALTR differ by bearing type with a significantly higher percentage of mixed type and solid type ALTR in the taper corrosion MoP THA compared with MoM THA. This information provides clinically useful information in evaluation of symptomatic MoP and MoM THA patients for ALTRs.

High union rates of locking compression plating with cortical strut allograft for type B1 periprosthetic femoral fractures.

PURPOSE: Unified classification system (UCS) type B1 periprosthetic femoral fractures are associated with many complications, and management decisions continue to be controversial. The purpose of this study was to evaluate outcomes of UCS type B1 periprosthetic femoral fractures treated by locking compression plating with strut allograft augmentation. MATERIALS AND METHODS: We retrospectively reviewed 17 consecutive UCS type B1 periprosthetic femoral fractures treated by open reduction and internal fixation using a lateral locking compression plate supplemented with an anterior cortical strut allograft. There was one man and 16 women with an average age of 74 years (range, 57-92 years). All had a cementless hip arthroplasty, and eight of the arthroplasties were revisions. RESULTS: The mean duration of follow-up was 28 months (range, 12-74 months). All 17 fractures healed successfully at a mean of 20 weeks (range, 12-30 weeks). The mean post-operative Harris hip score was 86 points (range, 77-95 points). No mechanical complications such as failure of plate or screws and malalignment were noted. According to the graft-remodeling classification of Emerson et al., a partial bridging was observed in nine and a complete bridging in eight. Two patients required a removal of the plate due to irritation of the iliotibial band. No femoral stem loosening or deep infection was observed. CONCLUSION: Our findings indicate that open reduction and internal fixation of UCS type B1 periprosthetic femoral fractures using a lateral locking compression plate supplemented with anterior cortical strut allograft provides adequate mechanical stability of fracture fixation and enhances the fracture healing.

Outcomes of total hip arthroplasty in patients with primary immune thrombocytopenia.

BACKGROUND: Immune thrombocytopenia (ITP) is an immune-mediated acquired disease that is characterized by a decrease in the platelet count and an increased risk of bleeding. There is little information in the literature about the results of major joint replacement surgery in patients with ITP. The aim of this study was to report on the results of total hip arthroplasty (THA) in patients with primary ITP. METHODS: We retrospectively identified 15 THAs performed in 11 patients with primary ITP. The study group was matched (1:2) to a non-ITP control group of 30 THAs in 22 patients. According to the perioperative hematologic evaluation, blood management interventions were performed. All procedures were performed by a single surgeon and all patients received cementless components with ceramic-on-ceramic bearing. Mean duration of follow-up was 7.1 years (range, 2-13). RESULTS: No significant differences were found between the two groups with regard to mean operative time, intraoperative blood loss, amount of closed suction drainage, length of hospital stay, and readmission rate. However, the proportion of patients requiring transfusion of packed red blood cells and/or platelet concentrate was higher in the ITP group when compared to the non-ITP group. Mean Harris hip score improved from 49.5 points preoperatively to 93.4 points at the final follow-up and no hips were revised for loosening or osteolysis in the ITP group. No significant differences were found between the two groups with respect to mean postoperative Harris hip scores and complication rates. CONCLUSIONS: Our study showed encouraging clinical and radiographic results of THA in patients with ITP without increased risk of adverse events compared to those in patients without ITP. On the basis of these findings, we suggest that modern cementless THA might be a viable treatment for achieving functional improvement in patients with ITP and end-stage hip disease.

The Use of Artificial Intelligence for the Prediction of Periprosthetic Joint Infection Following Aseptic Revision Total Knee Arthroplasty.

Periprosthetic joint infection (PJI) following revision total knee arthroplasty (TKA) for aseptic failure is associated with poor outcomes, patient morbidity, and high health care expenditures. The aim of this study was to develop novel machine learning algorithms for the prediction of PJI following revision TKA for patients with aseptic indications for revision surgery. A single-institution database consisting of 1,432 consecutive revision TKA patients with aseptic etiologies was retrospectively identified. The patient cohort included 208 patients (14.5%) who underwent re-revision surgery for PJI. Three machine learning algorithms (artificial neural networks, support vector machines, k-nearest neighbors) were developed to predict this outcome and these models were assessed by discrimination, calibration, and decision curve analysis. This is a retrospective study. Among the three machine learning models, the neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.78), calibration, and decision curve analysis. The strongest predictors for PJI following revision TKA for aseptic reasons were prior open procedure prior to revision surgery, drug abuse, obesity, and diabetes. This study utilized machine learning as a tool for the prediction of PJI following revision TKA for aseptic failure with excellent performance. The validated machine learning models can aid surgeons in patient-specific risk stratifying to assist in preoperative counseling and clinical decision making for patients undergoing aseptic revision TKA.

Can certain benign lesions of the proximal femur be treated without surgery?

BACKGROUND: Benign lesions in the proximal femur can cause pathologic fractures. To avoid fracture, benign tumors and tumor-like lesions in this region often are treated surgically, yet there have been few reports regarding the decision-making processes or protocols for nonsurgical treatment of these lesions. QUESTIONS/PURPOSES: In this study, we asked (1) whether some benign lesions of the proximal femur can be managed safely using a conservative protocol, and (2) if observed according to such a protocol, what are the outcomes of such lesions at this anatomic site? METHODS: Fifty-four consecutive patients who had been followed for at least 12 months were enrolled in this study. The mean age of the patients at first visit was 38 years (range, 13-70 years), and the minimum followup was 12 months (mean, 25 months; range, 12-59 months). After ruling out malignancy, lesions were categorized as aggressive benign tumors or nonaggressive benign lesions using a standardized approach. We used conservative treatment for most patients with nonaggressive, benign lesions. Surgery was performed only for patients with nonaggressive lesions who met our fracture risk criteria: pain on initiating hip movement, progressively worsening pain, cortical thinning, and the absence of a sclerotic margin. RESULTS: Of the 47 patients with a nonaggressive, benign lesion without fracture at presentation, 83% were treated conservatively and only 10% of these patients had progression of the lesion. No new pathologic fractures developed during followup. In 88% of patients who presented with pain that was managed conservatively, pain improved either partially or completely at final followup. CONCLUSIONS: Most nonaggressive, benign lesions in the proximal femur can be treated conservatively, and our protocol appears to be a useful outpatient guideline. LEVEL OF EVIDENCE: Level IV, therapeutic study. See the Guidelines for Authors for a complete description of levels of evidence.

The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA.

This is a retrospective study. Surgical site infection (SSI) is associated with adverse postoperative outcomes following total knee arthroplasty (TKA). However, accurately predicting SSI remains a clinical challenge due to the multitude of patient and surgical factors associated with SSI. This study aimed to develop and validate machine learning models for the prediction of SSI following primary TKA. This is a retrospective study for patients who underwent primary TKA. Chart review was performed to identify patients with superficial or deep SSIs, defined in concordance with the criteria of the Musculoskeletal Infection Society. All patients had a minimum follow-up of 2 years (range: 2.1-4.7 years). Five machine learning algorithms were developed to predict this outcome, and model assessment was performed by discrimination, calibration, and decision curve analysis. A total of 10,021 consecutive primary TKA patients was included in this study. At an average follow-up of 2.8 ± 1.1 years, SSIs were reported in 404 (4.0%) TKA patients, including 223 superficial SSIs and 181 deep SSIs. The neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.84), calibration, and decision curve analysis. The strongest predictors of the occurrence of SSI following primary TKA, in order, were Charlson comorbidity index, obesity (BMI >30 kg/m2), and smoking. The neural network model presented in this study represents an accurate method to predict patient-specific superficial and deep SSIs following primary TKA, which may be employed to assist in clinical decision-making to optimize outcomes in at-risk patients.

Artificial Neural Networks Can Predict Early Failure of Cementless Total Hip Arthroplasty in Patients With Osteoporosis.

BACKGROUND: Total hip arthroplasty (THA) done in the aging population is associated with osteoporosis-related complications. The altered bone density in osteoporotic patients is a risk factor for revision surgery. This study aimed to develop and validate machine learning (ML) models to predict revision surgery in patients with osteoporosis after primary noncemented THA. METHODS: We retrospectively reviewed a consecutive series of 350 patients with osteoporosis (T-score less than or equal to -2.5) who underwent primary noncemented THA at a tertiary referral center. All patients had a minimum 2-year follow-up (range: 2.1 to 5.6). Four ML algorithms were developed to predict the probability of revision surgery, and these were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The overall incidence of revision surgery was 5.2% at a mean follow-up of 3.7 years after primary noncemented THA in osteoporotic patients. Revision THA was done because of periprosthetic fracture in nine patients (50%), aseptic loosening/subsidence in five patients (28%), periprosthetic joint infection in two patients (11%) and dislocation in two patients (11%). The strongest predictors for revision surgery in patients after primary noncemented THA were female sex, BMI (>35 kg/m2), age (>70 years), American Society of Anesthesiology score (≥3), and T-score. All four ML models demonstrated good model performance across discrimination (AUC range: 0.78 to 0.81), calibration, and decision curve analysis. CONCLUSION: The ML models presented in this study demonstrated high accuracy for the prediction of revision surgery in osteoporotic patients after primary noncemented THA. The presented ML models have the potential to be used by orthopaedic surgeons for preoperative patient counseling and optimization to improve the outcomes of primary noncemented THA in osteoporotic patients.

Gait and Knee Flexion In Vivo Kinematics of Asymmetric Tibial Polyethylene Geometry Cruciate Retaining Total Knee Arthroplasty.

The preservation of the posterior cruciate ligament in cruciate retaining (CR) total knee arthroplasty (TKA) designs has the potential to restore healthy knee biomechanics; however, concerns related to kinematic asymmetries during functional activities still exist in unilateral TKA patients. As there is a limited data available regarding the ability of the contemporary CR TKA design with concave medial and convex lateral tibial polyethylene bearing components to restore healthy knee biomechanics, this study aimed to investigate in vivo three-dimensional knee kinematics in CR TKA patients during strenuous knee flexion activities and gait. Using a combined computer tomography and dual fluoroscopic imaging system approach, in vivo kinematics of 15 unilateral CR TKA patients (comparison of replaced and contralateral nonreplaced knee) were evaluated during sit-to-stand, step-ups, single-leg deep lunge, and level walking. The patient cohort was followed-up at an average of 24.5 months ( ± 12.6, range 13-42) from surgical procedure. Significantly smaller internal knee rotation angles were observed for the contemporary CR TKA design during step-ups (2.6 ± 5.8 vs. 6.3 ± 6.6 degrees, p < 0.05) and gait (0.6 ± 4.6 vs. 6.3 ± 6.8 degrees, p < 0.05). Significantly larger proximal and anterior femoral translations were measured during sit-to-stand (34.7 ± 4.5 vs. 29.9 ± 3.1 mm, p < 0.05; -2.5 ± 2.9 vs. -8.1 ± 4.4 mm, p < 0.05) and step-ups (34.1 ± 4.5 vs. 30.8 ± 2.9 mm, p < 0.05; 2.2 ± 3.2 vs. -3.5 ± 4.5 mm, p < 0.05). Significantly smaller ranges of varus/valgus and internal/external rotation range of motion were observed for CR TKA, when compared with the nonoperated nee, during strenuous activities and gait. The preservation of the posterior cruciate ligament in the contemporary asymmetric bearing geometry CR TKA design with concave medial and convex lateral tibial polyethylene bearing components has the potential to restore healthy knee biomechanics; however, the study findings demonstrate that native knee kinematics were not fully restored in patients with unilateral asymmetric tibial polyethylene bearing geometry CR TKA during functional activities.

The Utility of Machine Learning Algorithms for the Prediction of Early Revision Surgery After Primary Total Hip Arthroplasty.

BACKGROUND: Revision total hip arthroplasty (THA) is associated with increased morbidity, mortality, and healthcare costs due to a technically more demanding surgical procedure when compared with primary THA. Therefore, a better understanding of risk factors for early revision THA is essential to develop strategies for mitigating the risk of patients undergoing early revision. This study aimed to develop and validate novel machine learning (ML) models for the prediction of early revision after primary THA. METHODS: A total of 7,397 consecutive patients who underwent primary THA were evaluated, including 566 patients (6.6%) with confirmed early revision THA (<2 years from index THA). Electronic patient records were manually reviewed to identify patient demographics, implant characteristics, and surgical variables that may be associated with early revision THA. Six ML algorithms were developed to predict early revision THA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The strongest predictors for early revision after primary THA were Charlson Comorbidity Index, body mass index >35 kg/m2, and depression. The six ML models all achieved excellent performance across discrimination (area under the curve >0.80), calibration, and decision curve analysis. CONCLUSION: This study developed ML models for the prediction of early revision surgery for patients after primary THA. The study findings show excellent performance on discrimination, calibration, and decision curve analysis for all six candidate models, highlighting the potential of these models to assist in clinical practice patient-specific preoperative quantification of increased risk of early revision THA.

Radiographic Risk Factors Associated With Adverse Local Tissue Reaction in Head-Neck Taper Corrosion of Primary Metal-on-Polyethylene Total Hip Arthroplasty.

BACKGROUND: Adverse local tissue reactions (ALTRs) in metal-on-polyethylene (MoP) total hip arthroplasty (THA) with head-neck taper corrosion are multifactorial, involving implant and patient factors. This study aimed to identify any potential clinical risk factors associated with failed MoP THA due to head-neck taper corrosion. METHODS: A series of 146 MoP THA patients was investigated: (1) ALTR (n = 42) on metal artifact sequence MRI and (2) non-ALTR (n = 104). Both cohorts were compared regarding femoral neck shaft angle, acetabular implant orientation, component size, femoral head offset, measurement of medial and vertical femoral offsets, and femoral stem alloy. RESULTS: The occurrence of ALTR was associated with increased radiographic femoral stem offset (36.0 ± 7.7 mm versus 40.8 ± 7.3 mm, P = 0.008), increased femoral head offset (0.7 ± 3.4 versus 4.5 ± 3.7, P < 0.001), and the use of Ti-12Mo-6Zr-2Fe alloy stems (P = 0.041). The presence of ALTR was notably associated with higher chromium (2.0 versus 0.5 µg/L) and cobalt (7.4 versus 0.7 µg/L, P < 0.001). DISCUSSION: This study identified increased femoral head and stem offset and the use of Ti-12Mo-6Zr-2Fe alloy stems as risk factors for clinically relevant ALTR due to head-neck taper corrosion in MoP THA patients. This provides evidenced-based practical information for surgeons in identifying "at-risk" symptomatic MoP THA patients with head-neck taper corrosion for systematic risk stratification.

Effect of postural changes on in vivo pelvic tilt and functional component anteversion in total hip arthroplasty patients with lumbar disc degenerations.

AIMS: The complex relationship between acetabular component position and spinopelvic mobility in patients following total hip arthroplasty (THA) renders it difficult to optimize acetabular component positioning. Mobility of the normal lumbar spine during postural changes results in alterations in pelvic tilt (PT) to maintain the sagittal balance in each posture and, as a consequence, markedly changes the functional component anteversion (FCA). This study aimed to investigate the in vivo association of lumbar degenerative disc disease (DDD) with the PT angle and with FCA during postural changes in THA patients. METHODS: A total of 50 patients with unilateral THA underwent CT imaging for radiological evaluation of presence and severity of lumbar DDD. In all, 18 patients with lumbar DDD were compared to 32 patients without lumbar DDD. In vivo PT and FCA, and the magnitudes of changes (ΔPT; ΔFCA) during supine, standing, swing-phase, and stance-phase positions were measured using a validated dual fluoroscopic imaging system. RESULTS: PT, FCA, ΔPT, and ΔFCA were significantly correlated with the severity of lumbar DDD. Patients with severe lumbar DDD showed marked differences in PT with changes in posture; there was an anterior tilt (-16.6° vs -12.3°, p = 0.047) in the supine position, but a posterior tilt in an upright posture (1.0° vs -3.6°, p = 0.005). A significant decrease in ΔFCA during stand-to-swing (8.6° vs 12.8°, p = 0.038) and stand-to-stance (7.3° vs 10.6°,p = 0.042) was observed in the severe lumbar DDD group. CONCLUSION: There were marked differences in the relationship between PT and posture in patients with severe lumbar DDD compared with healthy controls. Clinical decision-making should consider the relationship between PT and FCA in order to reduce the risk of impingement at large ranges of motion in THA patients with lumbar DDD.

Head-Neck Taper Corrosion in Metal-on-Polyethylene Total Hip Arthroplasty: Risk Factors, Clinical Evaluation, and Treatment of Adverse Local Tissue Reactions.

Adverse local tissue reaction (ALTR) associated with mechanically assisted crevice corrosion of metal-on-polyethylene (MoP) head-neck modular total hip arthroplasty (THA), similarly observed in the metal-on-metal bearing, is a growing concern in MoP THA patients. Given the complex pathogenesis as well as variable clinical presentation, the diagnosis can be challenging. This article focuses on providing surgeons with an evidence-based update on (1) implant, surgical, and patient risk factors associated with ALTRs; (2) clinical systematic evaluation; and (3) surgical management options for ALTRs in MoP THA patients based on the currently available evidence.

Effect of postural changes on in vivo pelvic tilt and functional component anteversion in total hip arthroplasty patients with lumbar disc degenerations.

AIMS: The complex relationship between acetabular component position and spinopelvic mobility in patients following total hip arthroplasty (THA) renders it difficult to optimize acetabular component positioning. Mobility of the normal lumbar spine during postural changes results in alterations in pelvic tilt (PT) to maintain the sagittal balance in each posture and, as a consequence, markedly changes the functional component anteversion (FCA). This study aimed to investigate the in vivo association of lumbar degenerative disc disease (DDD) with the PT angle and with FCA during postural changes in THA patients. METHODS: A total of 50 patients with unilateral THA underwent CT imaging for radiological evaluation of presence and severity of lumbar DDD. In all, 18 patients with lumbar DDD were compared to 32 patients without lumbar DDD. In vivo PT and FCA, and the magnitudes of changes (ΔPT; ΔFCA) during supine, standing, swing-phase, and stance-phase positions were measured using a validated dual fluoroscopic imaging system. RESULTS: PT, FCA, ΔPT, and ΔFCA were significantly correlated with the severity of lumbar DDD. Patients with severe lumbar DDD showed marked differences in PT with changes in posture; there was an anterior tilt (-16.6° vs -12.3°, p = 0.047) in the supine position, but a posterior tilt in an upright posture (1.0° vs -3.6°, p = 0.005). A significant decrease in ΔFCA during stand-to-swing (8.6° vs 12.8°, p = 0.038) and stand-to-stance (7.3° vs 10.6°,p = 0.042) was observed in the severe lumbar DDD group. CONCLUSION: There were marked differences in the relationship between PT and posture in patients with severe lumbar DDD compared with healthy controls. Clinical decision-making should consider the relationship between PT and FCA in order to reduce the risk of impingement at large ranges of motion in THA patients with lumbar DDD. Cite this article: Bone Joint J 2020;102-B(11):1505-1510.

Risk factors for osteonecrosis of the femoral head in brain tumor patients receiving corticosteroid after surgery.

PURPOSE: Non-traumatic osteonecrosis of the femoral head (ONFH) is a plausible complication in brain tumor patients. Frequent use of corticosteroid therapy, chemotherapy, and oxidative stress for managing brain tumors may be associated with the development of ONFH. However, there is little knowledge on the prevalence and risk factors of ONFH from brain tumor. This study aimed to investigate the prevalence and risk factors of ONFH in patients with primary brain tumors. METHODS: This retrospective cohort study included data from consecutive patients between December 2005 and August 2016 from a tertiary university hospital in South Korea. A total of 73 cases of ONFH were identified among 10,674 primary brain tumor patients. After excluding subjects (25 out of 73) with missing data, history of alcohol consumption or smoking, history of femoral bone trauma or surgery, comorbidities such as systemic lupus erythematosus (SLE), sickle cell disease, cancer patients other than brain tumor, and previous diagnosis of contralateral ONFH, we performed a 1:2 propensity score-matched, case-control study (ONFH group, 48; control group, 96). Risk factors of ONFH in primary brain tumor were evaluated by univariate and multivariate logistic regression analyses. RESULTS: The prevalence of ONFH in patients with surgical resection of primary brain tumor was 683.9 per 100,000 persons (73 of 10,674). In this cohort, 55 of 74 patients (74.3%) underwent THA for ONFH treatment. We found that diabetes was an independent factor associated with an increased risk of ONFH in primary brain tumor patients (OR = 7.201, 95% CI, 1.349-38.453, p = 0.021). There was a significant difference in univariate analysis, including panhypopituitarism (OR = 4.394, 95% CI, 1.794-11.008, p = 0.002), supratentorial location of brain tumor (OR = 2.616, 95% CI, 1.245-5.499, p = 0.011), and chemotherapy (OR = 2.867, 95% CI, 1.018-8.069, p = 0.046). CONCLUSIONS: This study demonstrated that the prevalence of ONFH after surgical resection of primary brain tumor was 0.68%. Diabetes was an independent risk factor for developing ONFH, whereas corticosteroid dose was not. Routine screening for brain tumor-associated ONFH is not recommended; however, a high index of clinical suspicion in these patients at risk may allow for early intervention and preservation of the joints.