Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. Hip arthroscopy has gained prominence as a primary surgical intervention for symptomatic femoroacetabular impingement (FAI). This study aimed to identify radiological features, and their combinations, that predict the outcome of hip arthroscopy for FAI. Methods. A prognostic cross-sectional cohort study was conducted involving patients from a single centre who underwent hip arthroscopy between January 2013 and April 2021. Radiological metrics measured on conventional radiographs and magnetic resonance arthrography were systematically assessed. The study analyzed the relationship between these metrics and complication rates, revision rates, and patient-reported outcomes. Results. Out of 810 identified hip arthroscopies, 359 hips were included in the study. Radiological risk factors associated with unsatisfactory outcomes after cam resection included a dysplastic posterior wall, Tönnis grade 2 or higher, and over-correction of the α angle. The presence of acetabular retroversion and dysplasia were also significant predictors for worse surgical outcomes. Notably, over-correction of both cam and pincer deformities resulted in poorer outcomes than under-correction. Conclusion. We recommend caution in performing hip arthroscopy in patients who have three positive acetabular retroversion signs. Acetabular dysplasia with a lateral centre-edge angle of less than 20° should not be treated with isolated hip arthroscopy. Acetabular rim-trimming should be avoided in patients with borderline dysplasia, and care should be taken to avoid over-correction of a cam deformity and/or pincer deformity. Cite this article: Bone Joint J 2024;106-B(8):775–782

Aims. The preoperative grading of chondrosarcomas of bone that accurately predicts surgical management is difficult for surgeons, radiologists, and pathologists. There are often discrepancies in grade between the initial biopsy and the final histology. Recent advances in the use of imaging methods have shown promise in the ability to predict the final grade. The most important clinical distinction is between grade 1 chondrosarcomas, which are amenable to curettage, and resection-grade chondrosarcomas (grade 2 and 3) which require en bloc resection. The aim of this study was to evaluate the use of a Radiological Aggressiveness Score (RAS) to predict the grade of primary chondrosarcomas in long bones and thus to guide management. Methods. A total of 113 patients with a primary chondrosarcoma of a long bone presenting between January 2001 and December 2021 were identified on retrospective review of a single oncology centre’s prospectively collected database. The nine-parameter RAS included variables from radiographs and MRI scans. The best cut-off of parameters to predict the final grade of chondrosarcoma after resection was determined using a receiver operating characteristic curve (ROC), and this was correlated with the biopsy grade. Results. A RAS of ≥ four parameters was 97.9% sensitive and 90.5% specific in predicting resection-grade chondrosarcoma based on a ROC cut-off derived using the Youden index. Cronbach’s α of 0.897 was derived as the interclass correlation for scoring the lesions by four blinded reviewers who were surgeons. Concordance between resection-grade lesions predicted from the RAS and ROC cut-off with the final grade after resection was 96.46%. Concordance between the biopsy grade and the final grade was 63.8%. However, when the patients were analyzed based on surgical management, the initial biopsy was able to differentiate low-grade from resection-grade chondrosarcomas in 82.9% of biopsies. Conclusion. These findings suggest that the RAS is an accurate method for guiding the surgical management of patients with these tumours, particularly when the initial biopsy results are discordant with the clinical presentation. Cite this article: Bone Joint J 2023;105-B(7):808–814

Aims. The aim of this study was to assess the ability of morphological spinal parameters to predict the outcome of bracing in patients with adolescent idiopathic scoliosis (AIS) and to establish a novel supine correction index (SCI) for guiding bracing treatment. Methods. Patients with AIS to be treated by bracing were prospectively recruited between December 2016 and 2018, and were followed until brace removal. In all, 207 patients with a mean age at recruitment of 12.8 years (SD 1.2) were enrolled. Cobb angles, supine flexibility, and the rate of in-brace correction were measured and used to predict curve progression at the end of follow-up. The SCI was defined as the ratio between correction rate and flexibility. Receiver operating characteristic (ROC) curve analysis was carried out to assess the optimal thresholds for flexibility, correction rate, and SCI in predicting a higher risk of progression, defined by a change in Cobb angle of ≥ 5° or the need for surgery. Results. The baseline Cobb angles were similar (p = 0.374) in patients whose curves progressed (32.7° (SD 10.7)) and in those whose curves remained stable (31.4° (SD 6.1)). High supine flexibility (odds ratio (OR) 0.947 (95% CI 0.910 to 0.984); p = 0.006) and correction rate (OR 0.926 (95% CI 0.890 to 0.964); p < 0.001) predicted a lower incidence of progression after adjusting for Cobb angle, Risser sign, curve type, menarche status, distal radius and ulna grading, and brace compliance. ROC curve analysis identified a cut-off of 18.1% for flexibility (sensitivity 0.682, specificity 0.704) and a cut-off of 28.8% for correction rate (sensitivity 0.773, specificity 0.691) in predicting a lower risk of curve progression. A SCI of greater than 1.21 predicted a lower risk of progression (OR 0.4 (95% CI 0.251 to 0.955); sensitivity 0.583, specificity 0.591; p = 0.036). Conclusion. A higher supine flexibility (18.1%) and correction rate (28.8%), and a SCI of greater than 1.21 predicted a lower risk of progression. These novel parameters can be used as a guide to optimize the outcome of bracing. Cite this article: Bone Joint J 2022;104-B(4):495–503

Aims. We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Methods. Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature. Results. We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that RAB2A and FYCO1 were amplified in clinical samples. Conclusion. The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of RAB2A and FYCO1 underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring. Cite this article: Bone Joint Res 2022;11(8):548–560

Aims. The aim of this study was to evaluate the reliability and validity of a patient-specific algorithm which we developed for predicting changes in sagittal pelvic tilt after total hip arthroplasty (THA). Methods. This retrospective study included 143 patients who underwent 171 THAs between April 2019 and October 2020 and had full-body lateral radiographs preoperatively and at one year postoperatively. We measured the pelvic incidence (PI), the sagittal vertical axis (SVA), pelvic tilt, sacral slope (SS), lumbar lordosis (LL), and thoracic kyphosis to classify patients into types A, B1, B2, B3, and C. The change of pelvic tilt was predicted according to the normal range of SVA (0 mm to 50 mm) for types A, B1, B2, and B3, and based on the absolute value of one-third of the PI-LL mismatch for type C patients. The reliability of the classification of the patients and the prediction of the change of pelvic tilt were assessed using kappa values and intraclass correlation coefficients (ICCs), respectively. Validity was assessed using the overall mean error and mean absolute error (MAE) for the prediction of the change of pelvic tilt. Results. The kappa values were 0.927 (95% confidence interval (CI) 0.861 to 0.992) and 0.945 (95% CI 0.903 to 0.988) for the inter- and intraobserver reliabilities, respectively, and the ICCs ranged from 0.919 to 0.997. The overall mean error and MAE for the prediction of the change of pelvic tilt were -0.3° (SD 3.6°) and 2.8° (SD 2.4°), respectively. The overall absolute change of pelvic tilt was 5.0° (SD 4.1°). Pre- and postoperative values and changes in pelvic tilt, SVA, SS, and LL varied significantly among the five types of patient. Conclusion. We found that the proposed algorithm was reliable and valid for predicting the standing pelvic tilt after THA. Cite this article: Bone Joint J 2024;106-B(1):19–27

Aims. The aim of this study was to investigate the association between fracture displacement and survivorship of the native hip joint without conversion to a total hip arthroplasty (THA), and to determine predictors for conversion to THA in patients treated nonoperatively for acetabular fractures. Methods. A multicentre cross-sectional study was performed in 170 patients who were treated nonoperatively for an acetabular fracture in three level 1 trauma centres. Using the post-injury diagnostic CT scan, the maximum gap and step-off values in the weightbearing dome were digitally measured by two trauma surgeons. Native hip survival was reported using Kaplan-Meier curves. Predictors for conversion to THA were determined using Cox regression analysis. Results. Of 170 patients, 22 (13%) subsequently received a THA. Native hip survival in patients with a step-off ≤ 2 mm, > 2 to 4 mm, or > 4 mm differed at five-year follow-up (respectively: 94% vs 70% vs 74%). Native hip survival in patients with a gap ≤ 2 mm, > 2 to 4 mm, or > 4 mm differed at five-year follow-up (respectively: 100% vs 84% vs 78%). Step-off displacement > 2 mm (> 2 to 4 mm hazard ratio (HR) 4.9, > 4 mm HR 5.6) and age > 60 years (HR 2.9) were independent predictors for conversion to THA at follow-up. Conclusion. Patients with minimally displaced acetabular fractures who opt for nonoperative fracture treatment may be informed that fracture displacement (e.g. gap and step-off) up to 2 mm, as measured on CT images, results in limited risk on conversion to THA. Step-off ≥ 2 mm and age > 60 years are predictors for conversion to THA and can be helpful in the shared decision-making process. Cite this article: Bone Joint J 2023;105-B(9):1020–1029

Aims. Transfusion after primary total hip arthroplasty (THA) has become rare, and identification of causative factors allows preventive measures. The aim of this study was to determine patient-specific factors that increase the risk of needing a blood transfusion. Methods. All patients who underwent elective THA were analyzed retrospectively in this single-centre study from 2020 to 2021. A total of 2,892 patients were included. Transfusion-related parameters were evaluated. A multiple logistic regression was performed to determine whether age, BMI, American Society of Anesthesiologists (ASA) grade, sex, or preoperative haemoglobin (Hb) could predict the need for transfusion within the examined patient population. Results. The overall transfusion rate was 1.2%. Compared to the group of patients without blood transfusion, the transfused group was on average older (aged 73.8 years (SD 9.7) vs 68.6 years (SD 10.1); p = 0.020) and was mostly female (p = 0.003), but showed no significant differences in terms of BMI (28.3 kg/m. 2. (SD 5.9) vs 28.7 kg/m. 2. (SD 5.2); p = 0.720) or ASA grade (2.2 (SD 0.5) vs 2.1 (SD 0.4); p = 0.378). The regression model identified a cutoff Hb level of < 7.6 mmol/l (< 12.2 g/dl), aged > 73 years, and a BMI of 35.4 kg/m² or higher as the three most reliable predictors associated with postoperative transfusion in THA. Conclusion. The possibility of transfusion is predictable based on preoperatively available parameters. The proposed thresholds for preoperative Hb level, age, and BMI can help identify patients and take preventive measures if necessary. Cite this article: Bone Jt Open 2024;5(7):560–564

Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

Aims. The aims of this study were to validate the minimal clinically important difference (MCID) and patient-acceptable symptom state (PASS) thresholds for Western Ontario Shoulder Instability Index (WOSI), Rowe score, American Shoulder and Elbow Surgeons (ASES), and visual analogue scale (VAS) scores following arthroscopic Bankart repair, and to identify preoperative threshold values of these scores that could predict the achievement of MCID and PASS. Methods. A retrospective review was conducted on 131 consecutive patients with anterior shoulder instability who underwent arthroscopic Bankart repair between January 2020 and January 2023. Inclusion criteria required at least one episode of shoulder instability and a minimum follow-up period of 12 months. Preoperative and one-year postoperative scores were assessed. MCID and PASS were estimated using distribution-based and anchor-based methods, respectively. Receiver operating characteristic curve analysis determined preoperative patient-reported outcome measure thresholds predictive of achieving MCID and PASS. Results. MCID thresholds were determined as 169.6, 6.8, 7.2, and 1.1 for WOSI, Rowe, ASES, and VAS, respectively. PASS thresholds were calculated as ≤ 480, ≥ 80, ≥ 87, and ≤ 1 for WOSI, Rowe, ASES, and VAS, respectively. Preoperative thresholds of ≥ 760 (WOSI) and ≤ 50 (Rowe) predicted achieving MCID for WOSI score (p < 0.001). Preoperative thresholds of ≤ 60 (ASES) and ≥ 2 (VAS) predicted achieving MCID for VAS score (p < 0.001). A preoperative threshold of ≥ 40 (Rowe) predicted achieving PASS for Rowe score (p = 0.005). Preoperative thresholds of ≥ 50 (ASES; p = 0.002) and ≤ 2 (VAS; p < 0.001) predicted achieving PASS for the ASES score. Preoperative thresholds of ≥ 43 (ASES; p = 0.046) and ≤ 4 (VAS; p = 0.024) predicted achieving PASS for the VAS. Conclusion. This study defined MCID and PASS values for WOSI, Rowe, ASES, and VAS scores in patients undergoing arthroscopic Bankart repair. Higher preoperative functional scores may reduce the likelihood of achieving MCID but increase the likelihood of achieving the PASS. These findings provide valuable guidance for surgeons to counsel patients realistically regarding their expectations. Cite this article: Bone Joint J 2024;106-B(10):1118–1124

Aims. Periprosthetic fractures (PPFs) around the knee are challenging injuries. This study aims to describe the characteristics of knee PPFs and the impact of patient demographics, fracture types, and management modalities on in-hospital mortality. Methods. Using a multicentre study design, independent of registry data, we included adult patients sustaining a PPF around a knee arthroplasty between 1 January 2010 and 31 December 2019. Univariate, then multivariable, logistic regression analyses were performed to study the impact of patient, fracture, and treatment on mortality. Results. Out of a total of 1,667 patients in the PPF study database, 420 patients were included. The in-hospital mortality rate was 6.4%. Multivariable analyses suggested that American Society of Anesthesiologists (ASA) grade, history of peripheral vascular disease (PVD), history of rheumatic disease, fracture around a loose implant, and cerebrovascular accident (CVA) during hospital stay were each independently associated with mortality. Each point increase in ASA grade independently correlated with a four-fold greater mortality risk (odds ratio (OR) 4.1 (95% confidence interval (CI) 1.19 to 14.06); p = 0.026). Patients with PVD have a nine-fold increase in mortality risk (OR 9.1 (95% CI 1.25 to 66.47); p = 0.030) and patients with rheumatic disease have a 6.8-fold increase in mortality risk (OR 6.8 (95% CI 1.32 to 34.68); p = 0.022). Patients with a fracture around a loose implant (Unified Classification System (UCS) B2) have a 20-fold increase in mortality, compared to UCS A1 (OR 20.9 (95% CI 1.61 to 271.38); p = 0.020). Mode of management was not a significant predictor of mortality. Patients managed with revision arthroplasty had a significantly longer length of stay (median 16 days; p = 0.029) and higher rates of return to theatre, compared to patients treated nonoperatively or with fixation. Conclusion. The mortality rate in PPFs around the knee is similar to that for native distal femur and neck of femur fragility fractures. Patients with certain modifiable risk factors should be optimized. A national PPF database and standardized management guidelines are currently required to understand these complex injuries and to improve patient outcomes. Cite this article: Bone Joint J 2024;106-B(2):158–165

Aims. The aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA). Methods. A retrospective cohort of 5,857 patients was used to create the prediction models, and a second cohort of 721 patients from a different centre was used to validate the models, all of whom underwent TKA. Patient characteristics, BMI, OKS, and EQ-5D-3L were collected preoperatively and one year postoperatively. Generalized linear regression was used to formulate the prediction models. Results. There were significant correlations between the OKS and EQ-5D-3L preoperatively (r = 0.68; p < 0.001) and postoperatively (r = 0.77; p < 0.001) and for the change in the scores (r = 0.61; p < 0.001). Three different models (preoperative, postoperative, and change) were created. There were no significant differences between the actual and predicted mean EQ-5D-3L utilities at any timepoint or for change in the scores (p > 0.090) in the validation cohort. There was a significant correlation between the actual and predicted EQ-5D-3L utilities preoperatively (r = 0.63; p < 0.001) and postoperatively (r = 0.77; p < 0.001) and for the change in the scores (r = 0.56; p < 0.001). Bland-Altman plots demonstrated that a lower utility was overestimated, and higher utility was underestimated. The individual predicted EQ-5D-3L that was within ± 0.05 and ± 0.010 (minimal clinically important difference (MCID)) of the actual EQ-5D-3L varied between 13% to 35% and 26% to 64%, respectively, according to timepoint assessed and change in the scores, but was not significantly different between the modelling and validation cohorts (p ≥ 0.148). Conclusion. The OKS can be used to estimate EQ-5D-3L. Predicted individual patient utility error beyond the MCID varied from one-third to two-thirds depending on timepoint assessed, but the mean for a cohort did not differ and could be employed for this purpose. Cite this article: Bone Jt Open 2022;3(7):573–581

Aims. The aim of this study was to review the current evidence surrounding curve type and morphology on curve progression risk in adolescent idiopathic scoliosis (AIS). Methods. A comprehensive search was conducted by two independent reviewers on PubMed, Embase, Medline, and Web of Science to obtain all published information on morphological predictors of AIS progression. Search items included ‘adolescent idiopathic scoliosis’, ‘progression’, and ‘imaging’. The inclusion and exclusion criteria were carefully defined. Risk of bias of studies was assessed with the Quality in Prognostic Studies tool, and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. In all, 6,286 publications were identified with 3,598 being subjected to secondary scrutiny. Ultimately, 26 publications (25 datasets) were included in this review. Results. For unbraced patients, high and moderate evidence was found for Cobb angle and curve type as predictors, respectively. Initial Cobb angle > 25° and thoracic curves were predictive of curve progression. For braced patients, flexibility < 28% and limited in-brace correction were factors predictive of progression with high and moderate evidence, respectively. Thoracic curves, high apical vertebral rotation, large rib vertebra angle difference, small rib vertebra angle on the convex side, and low pelvic tilt had weak evidence as predictors of curve progression. Conclusion. For curve progression, strong and consistent evidence is found for Cobb angle, curve type, flexibility, and correction rate. Cobb angle > 25° and flexibility < 28% are found to be important thresholds to guide clinical prognostication. Despite the low evidence, apical vertebral rotation, rib morphology, and pelvic tilt may be promising factors. Cite this article: Bone Joint J 2022;104-B(4):424–432

Aims. Frailty has been gathering attention as a factor to predict surgical outcomes. However, the association of frailty with postoperative complications remains controversial in spinal metastases surgery. We therefore designed a prospective study to elucidate risk factors for postoperative complications with a focus on frailty. Methods. We prospectively analyzed 241 patients with spinal metastasis who underwent palliative surgery from June 2015 to December 2021. Postoperative complications were assessed by the Clavien-Dindo classification; scores of ≥ Grade II were defined as complications. Data were collected regarding demographics (age, sex, BMI, and primary cancer) and preoperative clinical factors (new Katagiri score, Frankel grade, performance status, radiotherapy, chemotherapy, spinal instability neoplastic score, modified Frailty Index-11 (mFI), diabetes, and serum albumin levels). Univariate and multivariate analyses were developed to identify risk factors for postoperative complications (p < 0.05). Results. Overall, 57 postoperative complications occurred in 47 of 241 (19.5%) patients. The most common complications were wound infection/dehiscence, urinary tract infection, and pneumonia. Univariate analysis identified preoperative radiotherapy (p = 0.028), mFI (p < 0.001), blood loss ≥ 500 ml (p = 0.016), and preoperative molecular targeted drugs (p = 0.030) as potential risk factors. From the receiver operating characteristic curve, the clinically optimal cut-off value of mFI was 0.27 (sensitivity, 46.8%; specificity, 79.9%). Multivariate analysis identified mFI ≥ 0.27 (odds ratio (OR) 2.94 (95% CI 1.44 to 5.98); p = 0.003) and preoperative radiotherapy (OR 2.11 (95% CI 1.00 to 4.46); p = 0.049) as significant risk factors. In particular, urinary tract infection (p = 0.012) and pneumonia (p = 0.037) were associated with mFI ≥ 0.27. Furthermore, the severity of postoperative complications was positively correlated with mFI (p < 0.001). Conclusion. The mFI is a useful tool to predict the incidence and the severity of postoperative complications in spinal metastases surgery. Cite this article: Bone Joint J 2024;106-B(12):1469–1476

Aims. The aims of this study were to determine if an increasing serum cobalt (Co) and/or chromium (Cr) concentration is correlated with a decreasing Harris Hip Score (HHS) and Hip disability and Osteoarthritis Outcome Score (HOOS) in patients who received the Articular Surface Replacement (ASR) hip resurfacing arthroplasty (HRA), and to evaluate the ten-year revision rate and show if sex, inclination angle, and Co level influenced the revision rate. Methods. A total of 62 patients with an ASR-HRA were included and monitored yearly postoperatively. At follow-up, serum Co and Cr levels were measured and the HHS and the HOOS were scored. In addition, preoperative patient and implant variables and the need for revision surgery were recorded. We used a linear mixed model to relate the serum Co and Cr levels to different patient-reported outcome measures (PROMs). For the survival analyses we used the Kaplan-Meier and Cox regression model. Results. We found that an increase of one part per billion (ppb) in serum Co and Cr levels correlated significantly with worsening of the HHS in the following year. This significant correlation was also true for the HOOS-Pain and HOOS-quality of life sub scores. The overall ten-year survival rate in our cohort was 65% (95% confidence interval (CI) 52.5 to 77.6). Cox regression analysis showed a significant hazard ratio (HR) of 1.08 (95% CI 1.01 to 1.15; p = 0.028) for serum Co level. No significance was found with sex or inclination angle. Conclusion. This study shows that increasing serum Co and Cr levels measured in patients with an ASR-HRA are predictive for deterioration in HHS and HOOS subscales in the following year. Increasing serum Co and Cr should forewarn both surgeon and patient that there is a heightened risk of failure. Continued and regular review of patients with an ASR-HRA implant by measurement of serum Co/Cr levels and PROMs remains essential. Cite this article: Bone Joint J 2023;105-B(7):775–782

Aims. This study examined windswept deformity (WSD) of the knee, comparing prevalence and contributing factors in healthy and osteoarthritic (OA) cohorts. Methods. A case-control radiological study was undertaken comparing 500 healthy knees (250 adults) with a consecutive sample of 710 OA knees (355 adults) undergoing bilateral total knee arthroplasty. The mechanical hip-knee-ankle angle (mHKA), medial proximal tibial angle (MPTA), and lateral distal femoral angle (LDFA) were determined for each knee, and the arithmetic hip-knee-ankle angle (aHKA), joint line obliquity, and Coronal Plane Alignment of the Knee (CPAK) types were calculated. WSD was defined as a varus mHKA of < -2° in one limb and a valgus mHKA of > 2° in the contralateral limb. The primary outcome was the proportional difference in WSD prevalence between healthy and OA groups. Secondary outcomes were the proportional difference in WSD prevalence between constitutional varus and valgus CPAK types, and to explore associations between predefined variables and WSD within the OA group. Results. WSD was more prevalent in the OA group compared to the healthy group (7.9% vs 0.4%; p < 0.001, relative risk (RR) 19.8). There was a significant difference in means and variance between the mHKA of the healthy and OA groups (mean -1.3° (SD 2.3°) vs mean -3.8°(SD 6.6°) respectively; p < 0.001). No significant differences existed in MPTA and LDFA between the groups, with a minimal difference in aHKA (mean -0.9° healthy vs -0.5° OA; p < 0.001). Backwards logistic regression identified meniscectomy, rheumatoid arthritis, and osteotomy as predictors of WSD (odds ratio (OR) 4.1 (95% CI 1.7 to 10.0), p = 0.002; OR 11.9 (95% CI 1.3 to 89.3); p = 0.016; OR 41.6 (95% CI 5.4 to 432.9), p ≤ 0.001, respectively). Conclusion. This study found a 20-fold greater prevalence of WSD in OA populations. The development of WSD is associated with meniscectomy, rheumatoid arthritis, and osteotomy. These findings support WSD being mostly an acquired condition following skeletal maturity. Cite this article: Bone Jt Open 2024;5(10):879–885

Aims. Intra-articular (IA) tumours around the knee are treated with extra-articular (EA) resection, which is associated with poor functional outcomes. We aim to evaluate the accuracy of MRI in predicting IA involvement around the knee. Methods. We identified 63 cases of high-grade sarcomas in or around the distal femur that underwent an EA resection from a prospectively maintained database (January 1996 to April 2020). Suspicion of IA disease was noted in 52 cases, six had IA pathological fracture, two had an effusion, two had prior surgical intervention (curettage/IA intervention), and one had an osseous metastasis in the proximal tibia. To ascertain validity, two musculoskeletal radiologists (R1, R2) reviewed the preoperative imaging (MRI) of 63 consecutive cases on two occasions six weeks apart. The radiological criteria for IA disease comprised evidence of tumour extension within the suprapatellar pouch, intercondylar notch, extension along medial/lateral retinaculum, and presence of IA fracture. The radiological predictions were then confirmed with the final histopathology of the resected specimens. Results. The resection histology revealed 23 cases (36.5%) showing IA disease involvement compared with 40 cases without (62%). The intraobserver variability of R1 was 0.85 (p < 0.001) compared to R2 with κ = 0.21 (p = 0.007). The interobserver variability was κ = 0.264 (p = 0.003). Knee effusion was found to be the most sensitive indicator of IA involvement, with a sensitivity of 91.3% but specificity of only 35%. However, when combined with a pathological fracture, this rose to 97.5% and 100% when disease was visible in Hoffa’s fat pad. Conclusion. MRI imaging can sometimes overestimate IA joint involvement and needs to be correlated with clinical signs. In the light of our findings, we would recommend EA resections when imaging shows effusion combined with either disease in Hoffa’s fat pad or retinaculum, or pathological fractures. Cite this article: Bone Joint J 2023;105-B(6):696–701

Aims. The most important complication of treatment of developmental dysplasia of the hip (DDH) is avascular necrosis (AVN) of the femoral head, which can result in proximal femoral growth disturbances leading to pain, dysfunction, and eventually to early onset osteoarthritis. In this study, we aimed to identify morphological variants in hip joint development that are predictive of a poor outcome. Methods. We retrospectively reviewed all patients who developed AVN after DDH treatment, either by closed and/or open reduction, at a single institution between 1984 and 2007 with a minimal follow-up of eight years. Standard pelvis radiographs obtained at ages one, two, three, five, and eight years, and at latest follow-up were retrieved. The Bucholz-Ogden classification was used to determine the type of AVN on all radiographs. Poor outcome was defined by Severin classification grade 3 or above on the latest follow-up radiographs and/or the need for secondary surgery. With statistical shape modelling, we identified the different shape variants of the hip at each age. Logistic regression analysis was used to associate the different modes or shape variants with poor outcome. Results. In all, 135 patients with AVN were identified, with a minimum of eight years of follow-up. Mean age at time of surgery was 7.0 months (SD 0.45), and mean follow-up was 13.3 years (SD 3.7). Overall, 46% had AVN type 1 while 54% type 2 or higher. More than half of the patients (52.6%) had a poor outcome. We found 11 shape variants that were significantly associated with a poor outcome. These shape variants were predominantly linked to AVN type 2 or higher. Conclusion. Specific morphological characteristics on pelvis radiographs of AVN hips were predictive for poor outcome, at a very young age. There was an overall stronger association to Bucholz-Ogden types 2-3-4 with the exception of two modes at age two and five years, linked to AVN type 1. Cite this article: Bone Joint J 2021;103-B(5):999–1004

Aims. Surgical site infection (SSI) after soft-tissue sarcoma (STS) resection is a serious complication. The purpose of this retrospective study was to investigate the risk factors for SSI after STS resection, and to develop a nomogram that allows patient-specific risk assessment. Methods. A total of 547 patients with STS who underwent tumour resection between 2005 and 2021 were divided into a development cohort and a validation cohort. In the development cohort of 402 patients, the least absolute shrinkage and selection operator (LASSO) regression model was used to screen possible risk factors of SSI. To select risk factors and construct the prediction nomogram, multivariate logistic regression was used. The predictive power of the nomogram was evaluated by receiver operating curve (ROC) analysis in the validation cohort of 145 patients. Results. LASSO regression analysis selected possible risk factors for SSI, including age, diabetes, operating time, skin graft or flap, resected tumour size, smoking, and radiation therapy. Multivariate analysis revealed that age, diabetes, smoking during the previous year, operating time, and radiation therapy were independent risk factors for SSI. A nomogram was developed based on the results of multivariate logistic regression analysis. In the development cohort, the incidence of SSI was 4.5% in the low-risk group (risk score < 6.89) and 26.6% in the high-risk group (risk score ≥ 6.89; p < 0.001). In the validation cohort, the incidence of SSI was 2.0% in the low-risk group and 15.9% in the high-risk group (p = 0.004). Conclusion. Our nomogram will enable surgeons to assess the risk of SSI in patients with STS. In patients with high risk of SSI, frequent monitoring and aggressive interventions should be considered to prevent this. Cite this article: Bone Joint J 2024;106-B(5):492–500

Aims. Hip fractures are some of the most common fractures encountered in orthopaedic practice. We aimed to identify whether perioperative hypotension is a predictor of 30-day mortality, and to stratify patient groups that would benefit from closer monitoring and early intervention. While there is literature on intraoperative blood pressure, there are limited studies examining pre- and postoperative blood pressure. Methods. We conducted a prospective observational cohort study over a one-year period from December 2021 to December 2022. Patient demographic details, biochemical results, and haemodynamic observations were taken from electronic medical records. Statistical analysis was conducted with the Cox proportional hazards model, and the effects of independent variables estimated with the Wald statistic. Kaplan-Meier survival curves were estimated with the log-rank test. Results. A total of 528 patients were identified as suitable for inclusion. On multivariate analysis, postoperative hypotension of a systolic blood pressure (SBP) < 90 mmHg two to 24 hours after surgery showed an increased hazard ratio (HR) for 30-day mortality (HR 4.6 (95% confidence interval (CI) 2.3 to 8.9); p < 0.001) and was an independent risk factor accounting for sex (HR 2.7 (95% CI 1.4 to 5.2); p = 0.003), age (HR 1.1 (95% CI 1.0 to 1.1); p = 0.016), American Society of Anesthesiologists grade (HR 2.7 (95% CI 1.5 to 4.6); p < 0.001), time to theatre > 24 hours (HR 2.1 (95% CI 1.1 to 4.2); p = 0.025), and preoperative anaemia (HR 2.3 (95% CI 1.0 to 5.2); p = 0.043). A preoperative SBP of < 120 mmHg was close to achieving significance (HR 1.9 (95% CI 0.99 to 3.6); p = 0.052). Conclusion. Our study is the first to demonstrate that postoperative hypotension within the first 24 hours is an independent risk factor for 30-day mortality after hip fracture surgery. Clinicians should recognize patients who have a SBP of < 90 mmHg in the early postoperative period, and be aware of the increased mortality risk in this specific cohort who may benefit from a closer level of monitoring and early intervention. Cite this article: Bone Joint J 2024;106-B(2):189–194