Complete blood platelet and lymphocyte ratios increase diagnostic accuracy of periprosthetic joint infection following total hip arthroplasty.

INTRODUCTION: Systemically, changes in serum platelet to lymphocyte ratio (PLR), platelet count to mean platelet volume ratio (PVR), neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte (MLR) represent primary responses to early inflammation and infection. This study aimed to determine whether PLR, PVR, NLR, and MLR can be useful in diagnosing periprosthetic joint infection (PJI) in total hip arthroplasty (THA) patients. METHODS: A total of 464 patients that underwent revision THA with calculable PLR, PVR, NLR, and MLR in 2 groups was evaluated: 1) 191 patients with a pre-operative diagnosis of PJI, and 2) 273 matched patients treated for revision THA for aseptic complications. RESULTS: The sensitivity and specificity of PLR combined with erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), synovial white blood cell count (WBC) and synovial polymorphonuclear leukocytes (PMN) (97.9%; 98.5%) is significantly higher than only ESR combined with CRP, synovial WBC and synovial PMN (94.2%; 94.5%; p < 0.01). The sensitivity and specificity of PVR combined with ESR, CRP and synovial WBC, and synovial PMN (98.4%; 98.2%) is higher than only ESR combined with CRP, synovial WBC and synovial PMN (94.2%; 94.5%; p < 0.01). CONCLUSION: The study results demonstrate that both PLR and PVR calculated from complete blood counts when combined with serum and synovial fluid markers have increased diagnostic sensitivity and specificity in diagnosing periprosthetic joint infection in THA patients. LEVEL OF EVIDENCE: III, case-control retrospective analysis.

The utilization of artificial neural networks for the prediction of 90-day unplanned readmissions following total knee arthroplasty.

BACKGROUND: A reliable predictive tool to predict unplanned readmissions has the potential to lower readmission rates through targeted pre-operative counseling and intervention with respect to modifiable risk factors. This study aimed to develop and internally validate machine learning models for the prediction of 90-day unplanned readmissions following total knee arthroplasty. METHODS: A total of 10,021 consecutive patients underwent total knee arthroplasty. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with 90-day unplanned hospital readmissions. Four machine learning algorithms (artificial neural networks, support vector machine, k-nearest neighbor, and elastic-net penalized logistic regression) were developed to predict 90-day unplanned readmissions following total knee arthroplasty and these models were evaluated using ROC AUC statistics as well as calibration and decision curve analysis. RESULTS: Within the study cohort, 644 patients (6.4%) were readmitted within 90 days. The factors most significantly associated with 90-day unplanned hospital readmissions included drug abuse, surgical operative time, and American Society of Anaesthesiologist Physical Status (ASA) score. The machine learning models all achieved excellent performance across discrimination (AUC > 0.82), calibration, and decision curve analysis. CONCLUSION: This study developed four machine learning models for the prediction of 90-day unplanned hospital readmissions in patients following total knee arthroplasty. The strongest predictors for unplanned hospital readmissions were drug abuse, surgical operative time, and ASA score. The study findings show excellent model performance across all four models, highlighting the potential of these models for the identification of high-risk patients prior to surgery for whom coordinated care efforts may decrease the risk of subsequent hospital readmission. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

Artificial neural networks for the prediction of transfusion rates in primary total hip arthroplasty.

BACKGROUND: Despite advancements in total hip arthroplasty (THA) and the increased utilization of tranexamic acid, acute blood loss anemia necessitating allogeneic blood transfusion persists as a post-operative complication. The prevalence of allogeneic blood transfusion in primary THA has been reported to be as high as 9%. Therefore, this study aimed to develop and validate novel machine learning models for the prediction of transfusion rates following primary total hip arthroplasty. METHODS: A total of 7265 consecutive patients who underwent primary total hip arthroplasty were evaluated using a single tertiary referral institution database. Patient charts were manually reviewed to identify patient demographics and surgical variables that may be associated with transfusion rates. Four state-of-the-art machine learning algorithms were developed to predict transfusion rates following primary THA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The factors most significantly associated with transfusion rates include tranexamic acid usage, bleeding disorders, and pre-operative hematocrit (< 33%). The four machine learning models all achieved excellent performance across discrimination (AUC > 0.78), calibration, and decision curve analysis. CONCLUSION: This study developed machine learning models for the prediction of patient-specific transfusion rates following primary total hip arthroplasty. The results represent a novel application of machine learning, and has the potential to improve outcomes and pre-operative planning. LEVEL OF EVIDENCE: 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.

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.

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.

Asymmetrical tibial polyethylene geometry-cruciate retaining total knee arthroplasty does not fully restore in-vivo articular contact kinematics during strenuous activities.

PURPOSE: A new CR TKA design with concave medial and convex lateral tibial polyethylene bearing components was introduced recently to improve functional outcomes. This study aimed to investigate in-vivo articular contact kinematics in unilateral asymmetrical tibial polyethylene geometry CR TKA patients during strenuous knee flexion activities. METHODS: Fifteen unilateral CR TKA patients (68.4 ± 5.8 years; 6 male/9 female) were evaluated for both knees during sit-to-stand, single-leg deep lunges and step-ups using validated combined computer tomography and dual fluoroscopic imaging system. Medial and lateral condylar contact positions were quantified during weight-bearing flexion activities. The Wilcoxon signed-rank test was performed to determine if there is a significant difference in articular contact kinematics during strenuous flexion activities between CR TKA and the non-operated knees. RESULTS: Contact excursions of the lateral condyle in CR TKAs were significantly more anteriorly located than the contralateral non-operated knee during sit-to-stand (3.7 ± 4.8 mm vs - 7.8 ± 4.3 mm) and step-ups (- 1.5 ± 3.2 mm vs - 6.3 ± 5.8 mm). Contact excursions of the lateral condyle in CR TKAs were significantly less laterally located than the contralateral non-operated knee during sit-to-stand (21.4 ± 2.8 mm vs 24.5 ± 4.7 mm) and single-leg deep lunges (22.6 ± 4.4 mm vs 26.2 ± 5.7 mm, p < 0.05). Lateral condyle posterior rollback was not fully restored in CR TKA patients during sit-to-stand (9.8 ± 6.7 mm vs 12.9 ± 8.3 mm) and step-ups (8.1 ± 4.8 mm vs 12.2 ± 6.4 mm). Lateral pivoting patterns were observed in 80%, 73% and 69% of patients during sit-to-stand, step-ups and single-leg deep lunges respectively. CONCLUSION: Although lateral femoral rollback and lateral pivoting patterns were observed during strenuous functional daily activities, asymmetric contact kinematics still persisted in unilateral CR TKA patients. This suggests the specific investigated contemporary asymmetrical tibial polyethylene geometry CR TKA design evaluated in this study does not fully replicate healthy knee contact kinematics during strenuous functional daily activities. LEVEL OF EVIDENCE: III.

Neural network models accurately predict discharge disposition after revision total knee arthroplasty?

PURPOSE: Based on the rising incidence of revision total knee arthroplasty (TKA), bundled payment models may be applied to revision TKA in the near future. Facility discharge represents a significant cost factor for those bundled payment models; however, accurately predicting discharge disposition remains a clinical challenge. The purpose of this study was to develop and validate artificial intelligence algorithms to predict discharge disposition following revision total knee arthroplasty. METHODS: A retrospective review of electronic patient records was conducted to identify patients who underwent revision total knee arthroplasty. Discharge disposition was defined as either home discharge or non-home discharge, which included rehabilitation and skilled nursing facilities. Four artificial intelligence algorithms were developed to predict this outcome and were assessed by discrimination, calibration and decision curve analysis. RESULTS: A total of 2228 patients underwent revision TKA, of which 1405 patients (63.1%) were discharged home, whereas 823 patients (36.9%) were discharged to a non-home facility. The strongest predictors for non-home discharge following revision TKA were American Society of Anesthesiologist (ASA) score, Medicare insurance type and revision surgery for peri-prosthetic joint infection, non-white ethnicity and social status (living alone). The best performing artificial intelligence algorithm was the neural network model which achieved excellent performance across discrimination (AUC = 0.87), calibration and decision curve analysis. CONCLUSION: This study developed four artificial intelligence algorithms for the prediction of non-home discharge disposition for patients following revision total knee arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four candidate algorithms. Therefore, these models have the potential to guide preoperative patient counselling and improve the value (clinical and functional outcomes divided by costs) of revision total knee arthroplasty patients. LEVEL OF EVIDENCE: IV.

Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty.

PURPOSE: Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA. METHODS: A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m2; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01). CONCLUSION: The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning. LEVEL OF EVIDENCE: IV.

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.

Metal Articulations as a Source of Total Hip Arthroplasty Pain.

The consensus systematic risk stratification algorithm from the American Association of Hip and Knee Surgeons, the American Academy of Orthopaedic Surgeons, and The Hip Society summarizes clinical challenges in evaluation and treatment of metal-on-polyethylene total hip arthroplasty (THA) patients with adverse local tissue reaction (ALTR) due to mechanically assisted crevice corrosion (MACC), reviews up-to-date evidence, and identifies the areas for future research in order to provide a useful resource for orthopedic surgeons providing care to these patients. A painful THA has various intrinsic and extrinsic causes. ALTR is one of the intrinsic causes in patients with painful THA. The occurrence of ALTR due to MACC at modular junctions is likely to be multifactorial, including implant, surgical, and patient factors. Therefore, a systematic evaluation needs to involve a focused clinical history, detailed physical examination, laboratory tests, and imaging in order to identify potential differential diagnoses. There should be a low threshold to perform a systematic evaluation of patients with painful non-metal-on-metal THA, including patients with metal-on-polyethylene THA, and modular dual-mobility THA with the CoCr metal acetabular insert, as early recognition and diagnosis of ALTR due to MACC will facilitate initiation of appropriate treatment prior to significant adverse biological reactions. Specialized tests such as blood metal analysis and metal artifact reduction sequence magnetic resonance imaging are important modalities in evaluation and management of ALTR in patients with painful THA.

Periprosthetic joint infection is the main reason for failure in patients following periprosthetic fracture treated with revision arthroplasty.

INTRODUCTION: Periprosthetic fracture after primary total hip and knee arthroplasty (THA; TKA) can be challenging, requiring open reduction internal fixation (ORIF), revision, or both. The aim of this study was to evaluate the outcomes and risk factors associated with re-revision surgery following failed revision arthroplasty for periprosthetic fracture. METHODS: A total of 316 consecutive THA patients and 79 consecutive TKA patients underwent a revision for periprosthetic fracture, of which 68 THA patients (21.5%) and 15 TKA patients (18.9%) underwent re-revision surgery. The most common indication for hip and knee re-revision was periprosthetic joint infection (PJI) in 28 THA patients (46.6%) and 11 TKA patients (47.8%). RESULTS: The complication rates of THA and TKA revision were 24.3% and 25.3% respectively, and 35.0% and 39.1% respectively for re-revision surgery at an average follow-up of 4.5 years. Periprosthetic joint infection was the most common indication for THA and TKA re-revision (46.7%; 47.8%) and third revision surgery (15.0%; 13.0%). Factors significantly contributing to an increased risk of THA and TKA re-revision included revision with plate fixation and revision with combined ORIF. CONCLUSION: The overall complication rate of THA and TKA re-revision surgery following failed revision surgery for periprosthetic fracture was higher than of revision surgery. The most common indication for re-revision and third revision was periprosthetic joint infection. These findings may assist surgeons in the management and preoperative counseling of patients undergoing THA and TKA revision surgery for a periprosthetic fracture to optimize the outcomes for these patients. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

Outcome and risk factors of failures associated with revision total hip arthroplasty for recurrent dislocation.

INTRODUCTION: Recurrent dislocation represents the third most common cause of revision surgery after total hip arthroplasty (THA). However, there is a paucity of information on the outcome of revision total hip arthroplasty for recurrent dislocation. In this study, we investigated (1) clinical outcomes of patients that underwent revision THA for recurrent dislocation, and (2) potential risk factors associated with treatment failure in patients who underwent revision total hip arthroplasty for recurrent dislocation. METHODS: We retrospectively reviewed 211 consecutive cases of revision total hip arthroplasty for recurrent dislocation, 81 implanted with a constrained liner and 130 with a non-constrained liner with a large-diameter femoral head (> 32 mm). Patient- and implant-related risk factors were analyzed in multivariate regression analysis. RESULTS: At 4.6-year follow-up, 32 of 211 patients (15.1%) underwent re-revision surgery. The most common causes for re-revision included infection (14 patients) and dislocation (10 patients). Kaplan-Meier analysis demonstrates a 5-year survival probability of 77% for patients that underwent revision THA for recurrent dislocation. Osteoporosis, obesity (BMI ≥ 40), spine disease and abductor deficiency are independent risk factors for failure of revision surgery for recurrent dislocation. Liner type (constrained vs. non-constrained) was found not to be associated with failure of revision THA for recurrent dislocation (p = 0.44). CONCLUSION: This study suggests that THA revision for recurrent dislocation is associated with a high re-revision rate of 15% at mid-term follow-up. Osteoporosis, obesity (BMI ≥ 40) spine disease and abductor deficiency were demonstrated to be independent risk factors for failure of revision THA for recurrent dislocation. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

Outcome of revision surgery for adverse local tissue reactions in patients with recalled total hip arthroplasty.

INTRODUCTION: Recalls of total hip arthroplasty (THA) implants, including metal-on-metal (MoM) THA and dual taper stems, due to increased risk of adverse local tissue reaction (ALTR), represent a challenge for both surgeons and patients. This study aims to analyze the revision surgery outcomes for ALTR in patients with recalled THA implants. METHODS: A total of 118 consecutive patients who underwent revision surgery due to ALTR with recalled THA were analyzed. Sub-group analysis was performed for recalled MoM THAs, head-neck modular stems, and dual taper neck-stems. RESULTS: At a mean follow-up of 6.6 years, the complication and reoperation rates of the recalled THAs were 32.2% and 25.4% respectively. The most common post-revision complication was dislocation (16%). Revision of modular taper corrosion THA and high-grade intraoperative tissue damage were risk factors associated with post-revision complications. CONCLUSION: This study reports high complication and reoperation rates of recalled THAs at mid-term follow-up. The high revision surgery complication rates in both groups suggest the importance of a systematic evaluation of all THA patients with at-risk implants. LEVEL OF EVIDENCE: Level III, case control retrospective analysis.

MARS MRI assessment of fatty degeneration of the gluteal muscles in patients with THA: reliability and accuracy of commonly used classification systems.

INTRODUCTION: Fatty degeneration of the gluteal muscles on metal artefact reduction sequence (MARS) MRI has been shown to correlate with poor functional outcomes, particularly in patients with total hip arthroplasty (THA). Standardized, reliable classification systems that permit assessment of fatty gluteal infiltration are needed for clinical decision making. This study aimed to compare the reproducibility and accuracy of commonly used MRI classification systems for fatty gluteal atrophy in THA patients. METHODS: MARS magnetic resonance images of 82 patients with unilateral THA were analysed by three independent trained observers. The readers evaluated fatty degeneration of the gluteus minimus, gluteus medius, and gluteus maximus according to 3 widely used classification systems: Goutallier, Quartile, and Bal and Lowe. Interobserver and intraobserver repeatability were determined using the weighted Kappa test. Quantitative evaluation of the proportion of intramuscular fat based on MR signal intensities was obtained and represented the gold standard. RESULTS: Mean interobserver agreement for the Quartile classification system (0.93) was higher compared with Goutallier classification system (0.87) and the Bal and Lowe classification system (0.83; range 0.79-0.88; p = 0.04). Intraobserver repeatability was significantly higher for the Quartile classification system (weighted kappa 0.91, 0.89, 0.85) compared with the Bal and Lowe classification system (weighted kappa 0.83, 0.77, 0.75; p < 0.01) and Goutallier classification system (weighted kappa 0.83, 0.77, 0.75; p = 0.04). Agreement with the gold standard measurements was significantly higher in the Quartile classification system (0.88, 0.84, 0.81) compared with the Goutallier classification system (0.80, 0.77, 0.78; p = 0.02) and Bal and Lowe classification system (0.76, 0.74, 0.73; p < 0.01). DISCUSSION: This study directly compared three clinically used MRI classification systems for fatty gluteal muscle atrophy in THA patients. Our findings demonstrate that although all three classification systems demonstrate good reproducibility and accuracy, the Quartile classification system is superior to the others in terms of intraobserver reliability and accuracy to quantify fatty gluteal degeneration in THA patients.

Development of a Preoperative Risk Calculator for Reinfection Following Revision Surgery for Periprosthetic Joint Infection.

BACKGROUND: A recent systematic review demonstrated that reinfection rates following eradication of hip and knee periprosthetic joint infection (PJI) may be as high as 29%. This study aimed to develop a preoperative risk calculator for assessing patient's individual risk associated with reinfection following treatment of PJI in total joint arthroplasty (TJA). METHODS: A total of 1081 consecutive patients who underwent revision TJA for PJI were evaluated. In total, 293 patients were diagnosed with TJA reinfection. A total of 56 risk factors, including patient characteristics and surgical variables, were evaluated with multivariate regression analysis. Analysis of the area under the receiver operating characteristics curve was performed to evaluate the strength of the predictive model. RESULTS: Of the 56 risk factors studied, 19 were found to have a significant effect as risk factor for TJA reinfection. The strongest predictors for TJA reinfection included previous PJI treatment techniques such as irrigation and debridement, the number of previous surgical interventions, medical comorbidities such as obesity, drug abuse, depression and smoking, as well as microbiology including the presence of Enterococcus species. The combined area under the receiver operating characteristics curve of the risk calculator for periprosthetic hip and knee joint reinfection was 0.75. CONCLUSIONS: The study findings demonstrate that surgical factors, including previous PJI surgical treatment techniques as well as the number of previous surgeries, alongside microbiology including the presence of Enterococcus species have the strongest effect on the risk for periprosthetic THA and TKA joint reinfection, suggesting the limited applicability of the existing risk calculators for the development of PJI following primary TJA in predicting the risk of periprosthetic joint reinfection.

Outcome of Revision Surgery for the Idiopathic Stiff Total Knee Arthroplasty.

BACKGROUND: Idiopathic stiff total knee arthroplasty (TKA) represents one of the most challenging subsets of the stiff TKA, as the etiology is unknown and there is no consensus on the most appropriate surgical treatment modality. Therefore, the aim of this study is to report on postoperative outcomes of revision surgery for idiopathic stiff TKA. METHODS: We retrospectively reviewed 189 consecutive patients (202 knees) who underwent revision TKA for stiffness: (1) 101 knees in the idiopathic stiffness cohort and (2) 88 in the non-idiopathic stiffness cohort. In the idiopathic stiffness cohort, 42 knees underwent isolated tibial insert exchange and 59 knees underwent component revision. Perioperative knee range of movement and complications were analyzed. RESULTS: The overall revision surgery outcomes of the idiopathic stiffness cohort were worse than those of the non-idiopathic stiffness cohort with regard to maximum flexion (91.7° vs 100.1°, P = .02) and flexion range of motion (ROM) (87.6° vs 97.1°, P = .01). In the idiopathic stiffness cohort, isolated tibial insert exchange demonstrated greater maximum flexion (96.8° vs 88.4°, P = .06) and flexion ROM (93.2° vs 83.9°, P = .07). In terms of re-revision rates, the isolated tibial insert exchange idiopathic stiffness cohort demonstrated lower re-revision rates compared to the component revision idiopathic stiffness cohort (16.7% vs 31.0%, P = .01). CONCLUSION: This study demonstrates that the overall revision surgery outcome of idiopathic stiff TKA is worse than non-idiopathic TKA stiffness. In idiopathic stiffness cohorts, isolated tibial insert exchange was associated with lower re-revision rates than component revision, with similar efficacy in improving ROM, suggesting that isolated tibial insert exchange may be a preferred surgical treatment option in TKA patients with idiopathic stiffness.

Concomitant Hip and Knee Periprosthetic Joint Infection in Periprosthetic Fracture: Diagnostic Utility of Serum and Synovial Fluid Markers.

BACKGROUND: Diagnosing periprosthetic joint infection (PJI) in patients with a periprosthetic fracture can be challenging due to concerns regarding the reliability of commonly used serum and synovial fluid markers. This study aimed at determining the diagnostic performance of serum and synovial fluid markers for diagnosing PJI in patients with a periprosthetic fracture of a total joint arthroplasty. METHODS: A total of 144 consecutive patients were included: (1) 41 patients with concomitant PJI and periprosthetic fracture and (2) 103 patients with periprosthetic fracture alone. Serum markers erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), and synovial markers white blood cell (WBC) count and polymorphonuclear percentage were assessed. RESULTS: ESR demonstrated 87% sensitivity and 48% specificity at the Musculoskeletal Infection Society threshold, area under the curve (AUC) of 0.74, and optimal threshold of 45.5 mm/h (76% sensitivity, 68% specificity). CRP showed 94% sensitivity and 40% specificity, AUC of 0.68 with optimal threshold of 16.7 mg/L (84% sensitivity, 51% specificity). Synovial WBC count demonstrated 87% sensitivity and 78% specificity, AUC of 0.90 with optimal threshold of 4552 cells/µL (86% sensitivity, 85% specificity). Polymorphonuclear percentage showed 79% sensitivity and 63% specificity, AUC of 0.70 with optimal threshold of 79.5% (74% sensitivity, 63% specificity). The AUC of all combined markers was 0.90 with 84% sensitivity and 79% specificity. CONCLUSION: The diagnostic utility of the serum and synovial markers for diagnosing PJI was lower in the setting of concomitant periprosthetic fracture compared to PJI alone. Using the Musculoskeletal Infection Society thresholds, ESR, CRP, and WBC count showed high sensitivity, yet low specificity, thus higher thresholds and utilizing all serum and synovial markers in combination should be considered.

Outcomes and Risk Factors Associated With 2-Stage Reimplantation Requiring an Interim Spacer Exchange for Periprosthetic Joint Infection.

BACKGROUND: Patients undergoing a 2-stage revision for periprosthetic joint infection (PJI) often require a repeat spacer in the interim due to persistent infection. This study aims to report outcomes for patients with repeat spacer exchange and to identify risk factors associated with interim spacer exchange in 2-stage revision arthroplasty. METHODS: A total of 256 consecutive 2-stage revisions for chronic infection of total hip arthroplasty and total knee arthroplasty with reimplantation and minimum 2-year follow-up were investigated. An interim spacer exchange was performed in 49 patients (exchange cohort), and these patients were propensity score matched to 196 patients (nonexchange cohort). Multivariate analysis was performed to analyze risk factors for failure of interim spacer exchange. RESULTS: Patients in the propensity score-matched exchange cohort demonstrated a significantly increased reinfection risk compared to patients without interim spacer exchange (24% vs 15%, P = .03). Patients in the propensity score-matched exchange cohort showed significantly lower postoperative scores for 3 patient-reported outcome measures (PROMs): hip disability and osteoarthritis outcome score physical function (46.0 vs 54.9, P = .01); knee disability and osteoarthritis outcome score physical function (43.1 vs 51.7, P < .01); and patient-reported outcomes measurement information system physical function short form (41.6 vs 47.0, P = .03). Multivariate analysis demonstrated Charles Comorbidity Index (odds ratio, 1.56; P = .01) and the presence of Enterococcus species (odds ratio, 1.43; P = .03) as independent risk factors associated with 2-stage reimplantation requiring an interim spacer exchange for periprosthetic joint infection. CONCLUSION: This study demonstrates that patients with spacer exchange had a significantly higher risk of reinfection at 2 years of follow-up. Additionally, patients with spacer exchange demonstrated lower postoperative PROM scores and diminished improvement in multiple PROM scores after reimplantation, indicating that an interim spacer exchange in 2-stage revision is associated with worse patient outcomes.