Aims. Treatment of high-grade limb bone sarcoma that invades a joint requires en bloc extra-articular excision. MRI can demonstrate joint invasion but is frequently inconclusive, and its predictive value is unknown. We evaluated the diagnostic accuracy of direct and indirect radiological signs of intra-articular tumour extension and the performance characteristics of MRI findings of intra-articular tumour extension. Methods. We performed a retrospective case-control study of patients who underwent extra-articular excision for sarcoma of the knee, hip, or shoulder from 1 June 2000 to 1 November 2020. Radiologists blinded to the pathology results evaluated preoperative MRI for three direct signs of joint invasion (capsular disruption, cortical breach, cartilage invasion) and indirect signs (e.g. joint effusion, synovial thickening). The discriminatory ability of MRI to detect intra-articular tumour extension was determined by receiver operating characteristic analysis. Results. Overall, 49 patients underwent extra-articular excision. The area under the curve (AUC) ranged from 0.65 to 0.76 for direct signs of joint invasion, and was 0.83 for all three combined. In all, 26 patients had only one to two direct signs of invasion, representing an equivocal result. In these patients, the AUC was 0.63 for joint effusion and 0.85 for synovial thickening. When direct signs and synovial thickening were combined, the AUC was 0.89. Conclusion. MRI provides excellent discrimination for determining intra-articular tumour extension when multiple direct signs of invasion are present. When MRI results are equivocal, assessment of synovial thickening increases MRI’s discriminatory ability to predict intra-articular joint extension. These results should be interpreted in the context of the study’s limitations. The inclusion of only extra-articular excisions enriched the sample for true positive cases. Direct signs likely varied with tumour histology and location. A larger, prospective study of periarticular bone sarcomas with spatial correlation of histological and radiological findings is needed to validate these results before their adoption in clinical practice. Cite this article: Bone Jt Open 2024;5(10):944–952

Aims. To report the development of the technique for minimally invasive lumbar decompression using robotic-assisted navigation. Methods. Robotic planning software was used to map out bone removal for a laminar decompression after registration of CT scan images of one cadaveric specimen. A specialized acorn-shaped bone removal robotic drill was used to complete a robotic lumbar laminectomy. Post-procedure advanced imaging was obtained to compare actual bony decompression to the surgical plan. After confirming accuracy of the technique, a minimally invasive robotic-assisted laminectomy was performed on one 72-year-old female patient with lumbar spinal stenosis. Postoperative advanced imaging was obtained to confirm the decompression. Results. A workflow for robotic-assisted lumbar laminectomy was successfully developed in a human cadaveric specimen, as excellent decompression was confirmed by postoperative CT imaging. Subsequently, the workflow was applied clinically in a patient with severe spinal stenosis. Excellent decompression was achieved intraoperatively and preservation of the dorsal midline structures was confirmed on postoperative MRI. The patient experienced improvement in symptoms postoperatively and was discharged within 24 hours. Conclusion. Minimally invasive robotic-assisted lumbar decompression utilizing a specialized robotic bone removal instrument was shown to be accurate and effective both in vitro and in vivo. The robotic bone removal technique has the potential for less invasive removal of laminar bone for spinal decompression, all the while preserving the spinous process and the posterior ligamentous complex. Spinal robotic surgery has previously been limited to the insertion of screws and, more recently, cages; however, recent innovations have expanded robotic capabilities to decompression of neurological structures. Cite this article: Bone Jt Open 2024;5(9):809–817

Aims. This study aimed to explore the diagnostic value of synovial fluid neutrophil extracellular traps (SF-NETs) in periprosthetic joint infection (PJI) diagnosis, and compare it with that of microbial culture, serum ESR and CRP, synovial white blood cell (WBC) count, and polymorphonuclear neutrophil percentage (PMN%). Methods. In a single health centre, patients with suspected PJI were enrolled from January 2013 to December 2021. The inclusion criteria were: 1) patients who were suspected to have PJI; 2) patients with complete medical records; and 3) patients from whom sufficient synovial fluid was obtained for microbial culture and NET test. Patients who received revision surgeries due to aseptic failure (AF) were selected as controls. Synovial fluid was collected for microbial culture and SF-WBC, SF-PNM%, and SF-NET detection. The receiver operating characteristic curve (ROC) of synovial NET, WBC, PMN%, and area under the curve (AUC) were obtained; the diagnostic efficacies of these diagnostic indexes were calculated and compared. Results. The levels of SF-NETs in the PJI group were significantly higher than those of the AF group. The AUC of SF-NET was 0.971 (95% confidence interval (CI) 0.903 to 0.996), the sensitivity was 93.48% (95% CI 82.10% to 98.63%), the specificity was 96.43% (95% CI 81.65% to 99.91%), the accuracy was 94.60% (95% CI 86.73% to 98.50%), the positive predictive value was 97.73%, and the negative predictive value was 90%. Further analysis showed that SF-NET could improve the diagnosis of culture-negative PJI, patients with PJI who received antibiotic treatment preoperatively, and fungal PJI. Conclusion. SF-NET is a novel and ideal synovial fluid biomarker for PJI diagnosis, which could improve PJI diagnosis greatly. Cite this article: Bone Joint Res 2023;12(2):113–120

Aims. This study aims to describe a new method that may be used as a supplement to evaluate humeral rotational alignment during intramedullary nail (IMN) insertion using the profile of the perpendicular peak of the greater tuberosity and its relation to the transepicondylar axis. We called this angle the greater tuberosity version angle (GTVA). Methods. This study analyzed 506 cadaveric humeri of adult patients. All humeri were CT scanned using 0.625 × 0.625 × 0.625 mm cubic voxels. The images acquired were used to generate 3D surface models of the humerus. Next, 3D landmarks were automatically calculated on each 3D bone using custom-written C++ software. The anatomical landmarks analyzed were the transepicondylar axis, the humerus anatomical axis, and the peak of the perpendicular axis of the greater tuberosity. Lastly, the angle between the transepicondylar axis and the greater tuberosity axis was calculated and defined as the GTVA. Results. The value of GTVA was 20.9° (SD 4.7°) (95% CI 20.47° to 21.3°). Results of analysis of variance revealed that females had a statistically significant larger angle of 21.95° (SD 4.49°) compared to males, which were found to be 20.49° (SD 4.8°) (p = 0.001). Conclusion. This study identified a consistent relationship between palpable anatomical landmarks, enhancing IMN accuracy by utilizing 3D CT scans and replicating a 20.9° angle from the greater tuberosity to the transepicondylar axis. Using this angle as a secondary reference may help mitigate the complications associated with malrotation of the humerus following IMN. However, future trials are needed for clinical validation. Cite this article: Bone Jt Open 2024;5(10):929–936

Aims. The aims of this study were to determine the diagnostic yield of image-guided biopsy in providing a final diagnosis in patients with suspected infectious spondylodiscitis, to report the diagnostic accuracy of various microbiological tests and histological examinations in these patients, and to report the epidemiology of infectious spondylodiscitis from a country where tuberculosis (TB) is endemic, including the incidence of drug-resistant TB. Methods. A total of 284 patients with clinically and radiologically suspected infectious spondylodiscitis were prospectively recruited into the study. Image-guided biopsy of the vertebral lesion was performed and specimens were sent for various microbiological tests and histological examinations. The final diagnosis was determined using a composite reference standard based on clinical, radiological, serological, microbiological, and histological findings. The overall diagnostic yield of the biopsy, and that for each test, was calculated in light of the final diagnosis. Results. The final diagnosis was tuberculous spondylodiscitis in 250 patients (88%) and pyogenic spondylodiscitis in 22 (7.8%). Six (2.1%) had a noninfectious condition-mimicking infectious spondylodiscitis, and six (2.1%) had no definite diagnosis and improved without specific treatment. The diagnosis was made by image-guided biopsy in 152 patients (56%) with infectious spondylodiscitis. Biopsy was contributory in identifying 132/250 patients (53%) with tuberculous spondylodiscitis, and 20/22 patients (91%) with pyogenic spondylodiscitis. Histological examination was the most sensitive diagnostic modality, followed by Xpert MTB/RIF assay. Conclusion. Image-guided biopsy has a reasonably high diagnostic yield in patients with suspected infectious spondylodiscitis. A combination of histological examination, Xpert MTB/RIF assay, bacterial culture, and sensitivity provides high diagnostic accuracy in a country in which TB is endemic. Cite this article: Bone Joint J 2022;104-B(1):120–126

Aims. The primary aim of this study was to compare the postoperative systemic inflammatory response in conventional jig-based total knee arthroplasty (conventional TKA) versus robotic-arm assisted total knee arthroplasty (robotic TKA). Secondary aims were to compare the macroscopic soft tissue injury, femoral and tibial bone trauma, localized thermal response, and the accuracy of component positioning between the two treatment groups. Methods. This prospective randomized controlled trial included 30 patients with osteoarthritis of the knee undergoing conventional TKA versus robotic TKA. Predefined serum markers of inflammation and localized knee temperature were collected preoperatively and postoperatively at six hours, day 1, day 2, day 7, and day 28 following TKA. Blinded observers used the Macroscopic Soft Tissue Injury (MASTI) classification system to grade intraoperative periarticular soft tissue injury and bone trauma. Plain radiographs were used to assess the accuracy of achieving the planned postioning of the components in both groups. Results. Patients undergoing conventional TKA and robotic TKA had comparable changes in the postoperative systemic inflammatory and localized thermal response at six hours, day 1, day 2, and day 28 after surgery. Robotic TKA had significantly reduced levels of interleukin-6 (p < 0.001), tumour necrosis factor-α (p = 0.021), ESR (p = 0.001), CRP (p = 0.004), lactate dehydrogenase (p = 0.007), and creatine kinase (p = 0.004) at day 7 after surgery compared with conventional TKA. Robotic TKA was associated with significantly improved preservation of the periarticular soft tissue envelope (p < 0.001), and reduced femoral (p = 0.012) and tibial (p = 0.023) bone trauma compared with conventional TKA. Robotic TKA significantly improved the accuracy of achieving the planned limb alignment (p < 0.001), femoral component positioning (p < 0.001), and tibial component positioning (p < 0.001) compared with conventional TKA. Conclusion. Robotic TKA was associated with a transient reduction in the early (day 7) postoperative inflammatory response but there was no difference in the immediate (< 48 hours) or late (day 28) postoperative systemic inflammatory response compared with conventional TKA. Robotic TKA was associated with decreased iatrogenic periarticular soft tissue injury, reduced femoral and tibial bone trauma, and improved accuracy of component positioning compared with conventional TKA. Cite this article: Bone Joint J 2021;103-B(1):113–122

Aims. This study reports mid-term outcomes after periacetabular osteotomy (PAO) exclusively in a borderline hip dysplasia (BHD) population to provide a contrast to published outcomes for arthroscopic surgery of the hip in BHD. Methods. We identified 42 hips in 40 patients treated between January 2009 and January 2016 with BHD defined as a lateral centre-edge angle (LCEA) of ≥ 18° but < 25°. A minimum five-year follow-up was available. Patient-reported outcomes (PROMs) including Tegner score, subjective hip value (SHV), modified Harris Hip Score (mHHS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were assessed. The following morphological parameters were evaluated: LCEA, acetabular index (AI), α angle, Tönnis staging, acetabular retroversion, femoral version, femoroepiphyseal acetabular roof index (FEAR), iliocapsularis to rectus femoris ratio (IC/RF), and labral and ligamentum teres (LT) pathology. Results. The mean follow-up was 96 months (67 to 139). The SHV, mHHS, WOMAC, and Tegner scores significantly improved (p < 0.001) at last follow-up. According to SHV and mHHS, there were three hips (7%) with poor results (SHV < 70), three (7%) with a fair score (70 to 79), eight (19%) with good results (80 to 89), and 28 (67%) who scored excellent (> 90) at the last follow-up. There were 11 subsequent operations: nine implant removals due to local irritation, one resection of postoperative heterotopic ossification, and one hip arthroscopy for intra-articular adhesions. No hips were converted to total hip arthroplasty at last follow-up. The presence of preoperative labral lesions or LT lesions did not influence any PROMs at last follow-up. From the three hips that had poor PROMs, two have developed severe osteoarthritis (> Tönnis II), presumably due to surgical overcorrection (postoperative AI < -10°). Conclusion. PAO is reliable in treating BHD with favourable mid-term outcomes. Concomitant LT and labral lesions did not negatively influence outcomes in our cohort. Technical accuracy with avoidance of overcorrection is essential in achieving successful outcomes. Cite this article: Bone Joint J 2023;105-B(7):735–742

Aims. The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. Methods. We developed CAT algorithms for the OHS, OKS, OSS, overall OES, and each of the OES subscales, using responses to the full-length questionnaires and a machine-learning technique called regression tree learning. The algorithms were evaluated through a series of simulation studies, in which they aimed to predict respondents’ full-length questionnaire scores from only a selection of their item responses. In each case, the total number of items used by the CAT algorithm was recorded and CAT scores were compared to full-length questionnaire scores by mean, SD, score distribution plots, Pearson’s correlation coefficient, intraclass correlation (ICC), and the Bland-Altman method. Differences between CAT scores and full-length questionnaire scores were contextualized through comparison to the instruments’ minimal clinically important difference (MCID). Results. The CAT algorithms accurately estimated 12-item questionnaire scores from between four and nine items. Scores followed a very similar distribution between CAT and full-length assessments, with the mean score difference ranging from 0.03 to 0.26 out of 48 points. Pearson’s correlation coefficient and ICC were 0.98 for each 12-item scale and 0.95 or higher for the OES subscales. In over 95% of cases, a patient’s CAT score was within five points of the full-length questionnaire score for each 12-item questionnaire. Conclusion. Oxford Hip Score, Oxford Knee Score, Oxford Shoulder Score, and Oxford Elbow Score (including separate subscale scores) CATs all markedly reduce the burden of items to be completed without sacrificing score accuracy. Cite this article: Bone Jt Open 2022;3(10):786–794

Aims. Although CT is considered the benchmark to measure femoral version, 3D biplanar radiography (hipEOS) has recently emerged as a possible alternative with reduced exposure to ionizing radiation and shorter examination time. The aim of our study was to evaluate femoral stem version in postoperative total hip arthroplasty (THA) patients and compare the accuracy of hipEOS to CT. We hypothesize that there will be no significant difference in calculated femoral stem version measurements between the two imaging methods. Methods. In this study, 45 patients who underwent THA between February 2016 and February 2020 and had both a postoperative CT and EOS scan were included for evaluation. A fellowship-trained musculoskeletal radiologist and radiological technician measured femoral version for CT and 3D EOS, respectively. Comparison of values for each imaging modality were assessed for statistical significance. Results. Comparison of the mean postoperative femoral stem version measurements between CT and 3D hipEOS showed no significant difference (p = 0.862). In addition, the two version measurements were strongly correlated (r = 0.95; p < 0.001), and the mean paired difference in postoperative femoral version for CT scan and 3D biplanar radiography was -0.09° (95% confidence interval -1.09 to 0.91). Only three stem measurements (6.7%) were considered outliers with a > 5° difference. Conclusion. Our study supports the use of low-dose biplanar radiography for the postoperative assessment of femoral stem version after THA, demonstrating high correlation with CT. We found no significant difference for postoperative femoral version when comparing CT to 3D EOS. We believe 3D EOS is a reliable option to measure postoperative femoral version given its advantages of lower radiation dosage and shorter examination time. Cite this article: Bone Joint J 2022;104-B(11):1196–1201

Aims. The aims of this study were: 1) to describe extended restricted kinematic alignment (E-rKA), a novel alignment strategy during robotic-assisted total knee arthroplasty (RA-TKA); 2) to compare residual medial compartment tightness following virtual surgical planning during RA-TKA using mechanical alignment (MA) and E-rKA, in the same set of osteoarthritic varus knees; 3) to assess the requirement of soft-tissue releases during RA-TKA using E-rKA; and 4) to compare the accuracy of surgical plan execution between knees managed with adjustments in component positioning alone, and those which require additional soft-tissue releases. Methods. Patients who underwent RA-TKA between January and December 2022 for primary varus osteoarthritis were included. Safe boundaries for E-rKA were defined. Residual medial compartment tightness was compared following virtual surgical planning using E-rKA and MA, in the same set of knees. Soft-tissue releases were documented. Errors in postoperative alignment in relation to planned alignment were compared between patients who did (group A) and did not (group B) require soft-tissue releases. Results. The use of E-rKA helped restore all knees within the predefined boundaries, with appropriate soft-tissue balancing. E-rKA compared with MA resulted in reduced residual medial tightness following surgical planning, in full extension (2.71 mm (SD 1.66) vs 5.16 mm (SD 3.10), respectively; p < 0.001), and 90° of flexion (2.52 mm (SD 1.63) vs 6.27 mm (SD 3.11), respectively; p < 0.001). Among the study population, 156 patients (78%) were managed with minor adjustments in component positioning alone, while 44 (22%) required additional soft-tissue releases. The mean errors in postoperative alignment were 0.53 mm and 0.26 mm among patients in group A and group B, respectively (p = 0.328). Conclusion. E-rKA is an effective and reproducible alignment strategy during RA-TKA, permitting a large proportion of patients to be managed without soft-tissue releases. The execution of minor alterations in component positioning within predefined multiplanar boundaries is a better starting point for gap management than soft-tissue releases. Cite this article: Bone Jt Open 2024;5(8):628–636

Aims. Iliopsoas impingement occurs in 4% to 30% of patients after undergoing total hip arthroplasty (THA). Despite a relatively high incidence, there are few attempts at modelling impingement between the iliopsoas and acetabular component, and no attempts at modelling this in a representative cohort of subjects. The purpose of this study was to develop a novel computational model for quantifying the impingement between the iliopsoas and acetabular component and validate its utility in a case-controlled investigation. Methods. This was a retrospective cohort study of patients who underwent THA surgery that included 23 symptomatic patients diagnosed with iliopsoas tendonitis, and 23 patients not diagnosed with iliopsoas tendonitis. All patients received postoperative CT imaging, postoperative standing radiography, and had minimum six months’ follow-up. 3D models of each patient’s prosthetic and bony anatomy were generated, landmarked, and simulated in a novel iliopsoas impingement detection model in supine and standing pelvic positions. Logistic regression models were implemented to determine if the probability of pain could be significantly predicted. Receiver operating characteristic curves were generated to determine the model’s sensitivity, specificity, and area under the curve (AUC). Results. Highly significant differences between the symptomatic and asymptomatic cohorts were observed for iliopsoas impingement. Logistic regression models determined that the impingement values significantly predicted the probability of groin pain. The simulation had a sensitivity of 74%, specificity of 100%, and an AUC of 0.86. Conclusion. We developed a computational model that can quantify iliopsoas impingement and verified its accuracy in a case-controlled investigation. This tool has the potential to be used preoperatively, to guide decisions about optimal cup placement, and postoperatively, to assist in the diagnosis of iliopsoas tendonitis. Cite this article: Bone Jt Open 2023;4(1):3–12

Aims. The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Methods. Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results. A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752. Conclusion. This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment. Cite this article: Bone Joint J 2023;105-B(6):702–710

Aims. The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. Methods. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. Results. The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an accuracy of 70%. The final model, which was built by fine-tuning a publicly available model named DenseNet, combining the AP and lateral radiographs, and incorporating information from the patient’s history, had an accuracy, sensitivity, and specificity of 88.3%, 70.2%, and 95.6% on the independent test dataset. It performed better for cases of revision THA with an accuracy of 90.1%, than for cases of revision TKA with an accuracy of 85.8%. Conclusion. This study showed that machine learning can detect prosthetic loosening from radiographs. Its accuracy is enhanced when using highly trained public algorithms, and when adding clinical data to the algorithm. While this algorithm may not be sufficient in its present state of development as a standalone metric of loosening, it is currently a useful augment for clinical decision making. Cite this article: Bone Joint J 2020;102-B(6 Supple A):101–106

Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. Results. A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34. o. (SD 2.4. o. ) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65. o. (SD 0.55. o. ) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. Conclusion. The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning. Cite this article: Bone Jt Open 2022;3(10):767–776

Aims. This study aimed to compare the performance of survival prediction models for bone metastases of the extremities (BM-E) with pathological fractures in an Asian cohort, and investigate patient characteristics associated with survival. Methods. This retrospective cohort study included 469 patients, who underwent surgery for BM-E between January 2009 and March 2022 at a tertiary hospital in South Korea. Postoperative survival was calculated using the PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models. Model performance was assessed with area under the curve (AUC), calibration curve, Brier score, and decision curve analysis. Cox regression analyses were performed to evaluate the factors contributing to survival. Results. The SORG model demonstrated the highest discriminatory accuracy with AUC (0.80 (95% confidence interval (CI) 0.76 to 0.85)) at 12 months. In calibration analysis, the PATHfx3.0 and OPTIModel models underestimated survival, while the SPRING13 and IOR models overestimated survival. The SORG model exhibited excellent calibration with intercepts of 0.10 (95% CI -0.13 to 0.33) at 12 months. The SORG model also had lower Brier scores than the null score at three and 12 months, indicating good overall performance. Decision curve analysis showed that all five survival prediction models provided greater net benefit than the default strategy of operating on either all or no patients. Rapid growth cancer and low serum albumin levels were associated with three-, six-, and 12-month survival. Conclusion. State-of-art survival prediction models for BM-E (PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models) are useful clinical tools for orthopaedic surgeons in the decision-making process for the treatment in Asian patients, with SORG models offering the best predictive performance. Rapid growth cancer and serum albumin level are independent, statistically significant factors contributing to survival following surgery of BM-E. Further refinement of survival prediction models will bring about informed and patient-specific treatment of BM-E. Cite this article: Bone Joint J 2024;106-B(2):203–211

Aims. To propose a new method for evaluating paediatric radial neck fractures and improve the accuracy of fracture angulation measurement, particularly in younger children, and thereby facilitate planning treatment in this population. Methods. Clinical data of 117 children with radial neck fractures in our hospital from August 2014 to March 2023 were collected. A total of 50 children (26 males, 24 females, mean age 7.6 years (2 to 13)) met the inclusion criteria and were analyzed. Cases were excluded for the following reasons: Judet grade I and Judet grade IVb (> 85° angulation) classification; poor radiograph image quality; incomplete clinical information; sagittal plane angulation; severe displacement of the ulna fracture; and Monteggia fractures. For each patient, standard elbow anteroposterior (AP) view radiographs and corresponding CT images were acquired. On radiographs, Angle P (complementary to the angle between the long axis of the radial head and the line perpendicular to the physis), Angle S (complementary to the angle between the long axis of the radial head and the midline through the proximal radial shaft), and Angle U (between the long axis of the radial head and the straight line from the distal tip of the capitellum to the coronoid process) were identified as candidates approximating the true coronal plane angulation of radial neck fractures. On the coronal plane of the CT scan, the angulation of radial neck fractures (CTa) was measured and served as the reference standard for measurement. Inter- and intraobserver reliabilities were assessed by Kappa statistics and intraclass correlation coefficient (ICC). Results. Angle U showed the strongest correlation with CTa (p < 0.001). In the analysis of inter- and intraobserver reliability, Kappa values were significantly higher for Angles S and U compared with Angle P. ICC values were excellent among the three groups. Conclusion. Angle U on AP view was the best substitute for CTa when evaluating radial neck fractures in children. Further studies are required to validate this method. Cite this article: Bone Joint J 2024;106-B(9):964–969

Limb alignment in total knee arthroplasty (TKA) influences periarticular soft-tissue tension, biomechanics through knee flexion, and implant survival. Despite this, there is no uniform consensus on the optimal alignment technique for TKA. Neutral mechanical alignment facilitates knee flexion and symmetrical component wear but forces the limb into an unnatural position that alters native knee kinematics through the arc of knee flexion. Kinematic alignment aims to restore native limb alignment, but the safe ranges with this technique remain uncertain and the effects of this alignment technique on component survivorship remain unknown. Anatomical alignment aims to restore predisease limb alignment and knee geometry, but existing studies using this technique are based on cadaveric specimens or clinical trials with limited follow-up times. Functional alignment aims to restore the native plane and obliquity of the joint by manipulating implant positioning while limiting soft tissue releases, but the results of high-quality studies with long-term outcomes are still awaited. The drawbacks of existing studies on alignment include the use of surgical techniques with limited accuracy and reproducibility of achieving the planned alignment, poor correlation of intraoperative data to long-term functional outcomes and implant survivorship, and a paucity of studies on the safe ranges of limb alignment. Further studies on alignment in TKA should use surgical adjuncts (e.g. robotic technology) to help execute the planned alignment with improved accuracy, include intraoperative assessments of knee biomechanics and periarticular soft-tissue tension, and correlate alignment to long-term functional outcomes and survivorship

Aims. Periacetabular osteotomy (PAO) is widely recognized as a demanding surgical procedure for acetabular reorientation. Reports about the learning curve have primarily focused on complication rates during the initial learning phase. Therefore, our aim was to assess the PAO learning curve from an analytical perspective by determining the number of PAOs required for the duration of surgery to plateau and the accuracy to improve. Methods. The study included 118 consecutive PAOs in 106 patients. Of these, 28 were male (23.7%) and 90 were female (76.3%). The primary endpoint was surgical time. Secondary outcome measures included radiological parameters. Cumulative summation analysis was used to determine changes in surgical duration. A multivariate linear regression model was used to identify independent factors influencing surgical time. Results. The learning curve in this series was 26 PAOs in a period of six months. After 26 PAO procedures, a significant drop in surgical time was observed and a plateau was also achieved. The mean duration of surgery during the learning curve was 103.8 minutes (SD 33.2), and 69.7 minutes (SD 18.6) thereafter (p < 0.001). Radiological correction of acetabular retroversion showed a significant improvement after having performed a total of 93 PAOs, including anteverting PAOs on 35 hips with a retroverted acetabular morphology (p = 0.005). Several factors were identified as independent variables influencing duration of surgery, including patient weight (β = 0.5 (95% confidence interval (CI) 0.2 to 0.7); p < 0.001), learning curve procedure phase of 26 procedures (β = 34.0 (95% CI 24.3 to 43.8); p < 0.001), and the degree of lateral correction expressed as the change in the lateral centre-edge angle (β = 0.7 (95% CI 0.001 to 1.3); p = 0.048). Conclusion. The learning curve for PAO surgery requires extensive surgical training at a high-volume centre, with a minimum of 50 PAOs per surgeon per year. This study defined a cut-off value of 26 PAO procedures, after which a significant drop in surgical duration occurred. Furthermore, it was observed that a retroverted morphology of the acetabulum required a greater number of procedures to acquire proficiency in consistently eliminating the crossover sign. These findings are relevant for fellows and fellowship programme directors in establishing the extent of training required to impart competence in PAO. Cite this article: Bone Joint J 2024;106-B(4):336–343

Aims. No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. Methods. A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. Results. The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. Conclusion. The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383–389

Objectives. The aim of this study was to assess the clinical application of, and optimize the variables used in, the BACH classification of long-bone osteomyelitis. Methods. A total of 30 clinicians from a variety of specialities classified 20 anonymized cases of long-bone osteomyelitis using BACH. Cases were derived from patients who presented to specialist centres in the United Kingdom between October 2016 and April 2017. Accuracy and Fleiss’ kappa (Fκ) were calculated for each variable. Bone involvement (B-variable) was assessed further by nine clinicians who classified ten additional cases of long bone osteomyelitis using a 3D clinical imaging package. Thresholds for defining multidrug-resistant (MDR) isolates were optimized using results from a further analysis of 253 long bone osteomyelitis cases. Results. The B-variable had a classification accuracy of 77.0%, which improved to 95.7% when using a 3D clinical imaging package (p < 0.01). The A-variable demonstrated difficulty in the accuracy of classification for increasingly resistant isolates (A1 (non-resistant), 94.4%; A2 (MDR), 46.7%; A3 (extensively or pan-drug-resistant), 10.0%). Further analysis demonstrated that isolates with four or more resistant test results or less than 80% sensitive susceptibility test results had a 98.1% (95% confidence interval (CI) 96.6 to 99.6) and 98.8% (95% CI 98.1 to 100.0) correlation with MDR status, respectively. The coverage of the soft tissues (C-variable) and the host status (H-variable) both had a substantial agreement between users and a classification accuracy of 92.5% and 91.2%, respectively. Conclusions. The BACH classification system can be applied accurately by users with a variety of clinical backgrounds. Accuracy of B-classification was improved using 3D imaging. The use of the A-variable has been optimized based on susceptibility testing results. Cite this article: A. J. Hotchen, M. Dudareva, J. Y. Ferguson, P. Sendi, M. A. McNally. The BACH classification of long bone osteomyelitis. Bone Joint Res 2019;8:459–468. DOI: 10.1302/2046-3758.810.BJR-2019-0050.R1