Aims. The aims of this study were to determine the success of a reconstruction algorithm used in major acetabular bone loss, and to further define the indications for custom-made implants in major acetabular bone loss. Methods. We reviewed a consecutive series of Paprosky type III acetabular defects treated according to a reconstruction algorithm. IIIA defects were planned to use a superior augment and hemispherical acetabular component. IIIB defects were planned to receive either a hemispherical acetabular component plus augments, a cup-cage reconstruction, or a custom-made implant. We used national digital health records and registry reports to identify any reoperation or re-revision procedure and Oxford Hip Score (OHS) for patient-reported outcomes. Implant survival was determined via Kaplan-Meier analysis. Results. A total of 105 procedures were carried out in 100 patients (five bilateral) with a mean age of 73 years (42 to 94). In the IIIA defects treated, 72.0% (36 of 50) required a porous metal augment; the remaining 14 patients were treated with a hemispherical acetabular component alone. In the IIIB defects, 63.6% (35 of 55) underwent reconstruction as planned with 20 patients who actually required a hemispherical acetabular component alone. At mean follow-up of 7.6 years, survival was 94.3% (95% confidence interval 97.4 to 88.1) for all-cause revision and the overall dislocation rate was 3.8% (4 of 105). There was no difference observed in survival between type IIIA and type IIIB defects and whether a hemispherical implant alone was used for the reconstruction or not. The mean gain in OHS was 16 points. Custom-made implants were only used in six cases, in patients with either a mega-defect in which the anteroposterior diameter > 80 mm, complex pelvic discontinuity, and massive bone loss in a small pelvis. Conclusion. Our findings suggest that a reconstruction algorithm can provide a successful approach to reconstruction in major acetabular bone loss. The use of custom implants has been defined in this series and accounts for < 5% of cases. Cite this article: Bone Joint J 2024;106-B(5 Supple B):47–53

In March 2012, an algorithm for the treatment of intertrochanteric fractures of the hip was introduced in our academic department of Orthopaedic Surgery. It included the use of specified implants for particular patterns of fracture. In this cohort study, 102 consecutive patients presenting with an intertrochanteric fracture were followed prospectively (post-algorithm group). Another 117 consecutive patients who had been treated immediately prior to the implementation of the algorithm were identified retrospectively as a control group (pre-algorithm group). The total cost of the implants prior to implementation of the algorithm was $357 457 (mean: $3055 (1947 to 4133)); compared with $255 120 (mean: $2501 (1052 to 4133)) after its implementation. There was a trend toward fewer complications in patients who were treated using the algorithm (33% pre- versus 22.5% post-algorithm; p = 0.088). Application of the algorithm to the pre-algorithm group revealed a potential overall cost saving of $70 295. The implementation of an evidence-based algorithm for the treatment of intertrochanteric fractures reduced costs while maintaining quality of care with a lower rate of complications and re-admissions. Cite this article: Bone Joint J 2014;96-B:1192–7

Aims. To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials. Methods. This study included 2,388 patients from the HEALTH and FAITH trials, with 90-day and one-year mortality proportions of 3.0% (71/2,388) and 6.4% (153/2,388), respectively. The mean age was 75.9 years (SD 10.8) and 65.9% of patients (1,574/2,388) were female. The algorithms included patient and injury characteristics. Six algorithms were developed, internally validated and evaluated across discrimination (c-statistic; discriminative ability between those with risk of mortality and those without), calibration (observed outcome compared to the predicted probability), and the Brier score (composite of discrimination and calibration). Results. The developed algorithms distinguished between patients at high and low risk for 90-day and one-year mortality. The penalized logistic regression algorithm had the best performance metrics for both 90-day (c-statistic 0.80, calibration slope 0.95, calibration intercept -0.06, and Brier score 0.039) and one-year (c-statistic 0.76, calibration slope 0.86, calibration intercept -0.20, and Brier score 0.074) mortality prediction in the hold-out set. Conclusion. Using high-quality data, the ML-based prediction models accurately predicted 90-day and one-year mortality in patients aged 50 years or older with a FNF. The final models must be externally validated to assess generalizability to other populations, and prospectively evaluated in the process of shared decision-making. Cite this article: Bone Jt Open 2023;4(3):168–181

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. A recent study used the RAND Corporation at University of California, Los Angeles (RAND/UCLA) method to develop anatomical total shoulder arthroplasty (aTSA) appropriateness criteria. The purpose of our study was to determine how patient-reported outcome measures (PROMs) vary based on appropriateness. Methods. Clinical data from a multicentre database identified patients who underwent primary aTSA from November 2004 to January 2023. A total of 390 patients (mean follow-up 48.1 months (SD 42.0)) were included: 97 (24.9%) were classified as appropriate, 218 (55.9%) inconclusive, and 75 (19.2%) inappropriate. Patients were classified as “appropriate”, “inconclusive”, or “inappropriate”, using a modified version of an appropriateness algorithm, which accounted for age, rotator cuff status, mobility, symptomatology, and Walch classification. Multiple pre- and postoperative scores were analyzed using Pearson’s chi-squared test and one-way analysis of variance (ANOVA). Postoperative complications were also analyzed. Results. All groups achieved significant improvement in mean PROM scores postoperatively. “Appropriate” patients experienced significantly greater improvement in visual analogue scale (VAS) and American Shoulder and Elbow Surgeons (ASES) score compared to “inconclusive” and “inappropriate”. The appropriate group had a significantly greater proportion of patients who achieved minimal clinically important difference (MCID) (95.8%; n = 93) and substantial clinical benefit (SCB) (92.6%; n = 89). Overall, 13 patients had postoperative complications. No significant differences in postoperative complications among classifications were found. Conclusion. Our data clinically validate the RAND/UCLA aTSA appropriateness criteria algorithm, allowing for more rapid and reliable determination of aTSA candidacy. “Appropriate” patients were more likely to achieve MCID and SCB for ASES scores compared to “inappropriate” patients. Among “appropriate” patients who did not achieve SCB, 50% (n = 4) had a postoperative complication. There was a significantly higher proportion of postoperative complications among those who did not achieve SCB across all three groups. Only 7.1% (n = 1) of patients who did not achieve SCB in the inappropriate group had a postoperative complication. Thus, it can be inferred that the failure to reach SCB in the appropriate group was likely to be due to a postoperative complication, whereas for patients deemed “inappropriate”, failure to reach SCB may be secondary to factors accounted for within our algorithm. Cite this article: Bone Joint J 2024;106-B(12):1451–1460

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 management of mid-shaft clavicle fractures (MSCFs) has evolved over the last three decades. Controversy exists over which specific fracture patterns to treat and when. This review aims to synthesize the literature in order to formulate an appropriate management algorithm for these injuries in both adolescents and adults. Methods. This is a systematic review of clinical studies comparing the outcomes of operative and nonoperative treatments for MSCFs in the past 15 years. The literature was searched using, PubMed, Google scholar, OVID Medline, and Embase. All databases were searched with identical search terms: mid-shaft clavicle fractures (± fixation) (± nonoperative). Results. Using the search criteria identified, 247 studies were deemed eligible. Following initial screening, 220 studies were excluded on the basis that they were duplicates and/or irrelevant to the research question being posed. A total of 27 full-text articles remained and were included in the final review. The majority of the meta-analyses draw the same conclusions, which are that operatively treated fractures have lower nonunion and malunion rates but that, in those fractures which unite (either operative or nonoperative), the functional outcomes are the same at six months. Conclusion. With regard to the adolescent population, the existing body of evidence is insufficient to support the use of routine operative management. Regarding adult fractures, the key to identifying patients who benefit from operative management lies in the identification of risk factors for nonunion. We present an algorithm that can be used to guide both the patient and the surgeon in a joint decision-making process, in order to optimize patient satisfaction and outcomes. Cite this article: Bone Jt Open 2022;3(11):850–858

Aims. There remains a lack of consensus regarding the management of chronic anterior sternoclavicular joint (SCJ) instability. This study aimed to assess whether a standardized treatment algorithm (incorporating physiotherapy and surgery and based on the presence of trauma) could successfully guide management and reduce the number needing surgery. Methods. Patients with chronic anterior SCJ instability managed between April 2007 and April 2019 with a standardized treatment algorithm were divided into non-traumatic (offered physiotherapy) and traumatic (offered surgery) groups and evaluated at discharge. Subsequently, midterm outcomes were assessed via a postal questionnaire with a subjective SCJ stability score, Oxford Shoulder Instability Score (OSIS, adapted for the SCJ), and pain visual analogue scale (VAS), with analysis on an intention-to-treat basis. Results. A total of 47 patients (50 SCJs, three bilateral) responded for 75% return rate. Of these, 31 SCJs were treated with physiotherapy and 19 with surgery. Overall, 96% (48/50) achieved a stable SCJ, with 60% (30/50) achieving unrestricted function. In terms of outcomes, 82% (41/50) recorded good-to-excellent OSIS scores (84% (26/31) physiotherapy, 79% (15/19) surgery), and 76% (38/50) reported low pain VAS scores at final follow-up. Complications of the total surgical cohort included a 19% (5/27) revision rate, 11% (3/27) frozen shoulder, and 4% (1/27) scar sensitivity. Conclusion. This is the largest midterm series reporting chronic anterior SCJ instability outcomes when managed according to a standardized treatment algorithm that emphasizes the importance of appropriate patient selection for either physiotherapy or surgery, based on a history of trauma. All but two patients achieved a stable SCJ, with stability maintained at a median of 70 months (11 to 116) for the physiotherapy group and 87 months (6 to 144) for the surgery group. Cite this article: Bone Jt Open 2022;3(10):815–825

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. Once knee arthritis and deformity have occurred, it is currently not known how to determine a patient’s constitutional (pre-arthritic) limb alignment. The purpose of this study was to describe and validate the arithmetic hip-knee-ankle (aHKA) algorithm as a straightforward method for preoperative planning and intraoperative restoration of the constitutional limb alignment in total knee arthroplasty (TKA). Methods. A comparative cross-sectional, radiological study was undertaken of 500 normal knees and 500 arthritic knees undergoing TKA. By definition, the aHKA algorithm subtracts the lateral distal femoral angle (LDFA) from the medial proximal tibial angle (MPTA). The mechanical HKA (mHKA) of the normal group was compared to the mHKA of the arthritic group to examine the difference, specifically related to deformity in the latter. The mHKA and aHKA were then compared in the normal group to assess for differences related to joint line convergence. Lastly, the aHKA of both the normal and arthritic groups were compared to test the hypothesis that the aHKA can estimate the constitutional alignment of the limb by sharing a similar centrality and distribution with the normal population. Results. There was a significant difference in means and distributions of the mHKA of the normal group compared to the arthritic group (mean -1.33° (SD 2.34°) vs mean -2.88° (SD 7.39°) respectively; p < 0.001). However, there was no significant difference between normal and arthritic groups using the aHKA (mean -0.87° (SD 2.54°) vs mean -0.77° (SD 2.84°) respectively; p = 0.550). There was no significant difference in the MPTA and LDFA between the normal and arthritic groups. Conclusion. The arithmetic HKA effectively estimated the constitutional alignment of the lower limb after the onset of arthritis in this cross-sectional population-based analysis. This finding is of significant importance to surgeons aiming to restore the constitutional alignment of the lower limb during TKA. Cite this article: Bone Jt Open 2021;2(5):351–358

Aims. We aimed to assess the reliability and validity of OpenPose, a posture estimation algorithm, for measurement of knee range of motion after total knee arthroplasty (TKA), in comparison to radiography and goniometry. Methods. In this prospective observational study, we analyzed 35 primary TKAs (24 patients) for knee osteoarthritis. We measured the knee angles in flexion and extension using OpenPose, radiography, and goniometry. We assessed the test-retest reliability of each method using intraclass correlation coefficient (1,1). We evaluated the ability to estimate other measurement values from the OpenPose value using linear regression analysis. We used intraclass correlation coefficients (2,1) and Bland–Altman analyses to evaluate the agreement and error between radiography and the other measurements. Results. OpenPose had excellent test-retest reliability (intraclass correlation coefficient (1,1) = 1.000). The R. 2. of all regression models indicated large correlations (0.747 to 0.927). In the flexion position, the intraclass correlation coefficients (2,1) of OpenPose indicated excellent agreement (0.953) with radiography. In the extension position, the intraclass correlation coefficients (2,1) indicated good agreement of OpenPose and radiography (0.815) and moderate agreement of goniometry with radiography (0.593). OpenPose had no systematic error in the flexion position, and a 2.3° fixed error in the extension position, compared to radiography. Conclusion. OpenPose is a reliable and valid tool for measuring flexion and extension positions after TKA. It has better accuracy than goniometry, especially in the extension position. Accurate measurement values can be obtained with low error, high reproducibility, and no contact, independent of the examiner’s skills. Cite this article: Bone Joint Res 2023;12(5):313–320

Aims. In 2013, we introduced a specialized, centralized, and interdisciplinary team in our institution that applied a standardized diagnostic and treatment algorithm for the management of prosthetic joint infections (PJIs). The hypothesis for this study was that the outcome of treatment would be improved using this approach. Patients and Methods. In a retrospective analysis with a standard postoperative follow-up, 95 patients with a PJI of the hip and knee who were treated with a two-stage exchange between 2013 and 2017 formed the study group. A historical cohort of 86 patients treated between 2009 and 2011 not according to the standardized protocol served as a control group. The success of treatment was defined according to the Delphi criteria in a two-year follow-up. Results. Patients in the study group had a significantly higher Charlson Comorbidity Index (3.9 vs 3.1; p = 0.009) and rate of previous revisions for infection (52.6% vs 36%; p = 0.025), and tended to be older (69.0 vs 66.2 years; p = 0.075) with a broader polymicrobial spectrum (47.3% vs 33.7%; p = 0.062). The rate of recurrent infection (3.1% vs 10.4%; p = 0.048) and the mean time interval between the two stages of the procedure (66.6 vs 80.7 days; p < 0.001) were reduced significantly in the study group compared with the control group. Conclusion. We were able to show that the outcome following the treatment of PJIs of the hip and knee is better when managed in a separate department with an interdisciplinary team using a standard algorithm

Aims. The Oswestry-Bristol Classification (OBC) is an MRI-specific assessment tool to grade trochlear dysplasia. The aim of this study is to validate clinically the OBC by demonstrating its use in selecting treatments that are safe and effective. Methods. The OBC and the patellotrochlear index were used as part of the Oswestry Patellotrochlear Algorithm (OPTA) to guide the surgical treatment of patients with patellar instability. Patients were assigned to one of four treatment groups: medial patellofemoral ligament reconstruction (MPFLr); MPFLr + tibial tubercle distalization (TTD); trochleoplasty; or trochleoplasty + TTD. A prospective analysis of a longitudinal patellofemoral database was performed. Between 2012 and 2018, 202 patients (233 knees) with a mean age of 24.2 years (SD 8.1), with recurrent patellar instability were treated by two fellowship-trained consultant sports/knee surgeons at The Robert Jones and Agnes Hunt Orthopaedic Hospital. Clinical efficacy of each treatment group was assessed by Kujala, International Knee Documentation Committee (IKDC), and EuroQol five-dimension questionnaire (EQ-5D) scores at baseline, and up to 60 months postoperatively. Their safety was assessed by complication rate and requirement for further surgery. The pattern of clinical outcome over time was analyzed using mixed regression modelling. Results. In all, 135 knees (mean age 24.9 years (SD 9.4)) were treated using a MPFLr. Ten knees (7.4%) required additional surgery. A total of 50 knees (mean age 24.4 years (SD 6.3)) were treated using MPFLr + TTD. Ten (20%) required additional surgery. A total of 20 knees (mean age 19.5 years (SD 3.0)) were treated using trochleoplasty + TTD. Three patients (15%) required additional surgery. In each treatment group, there was a significant improvement in Kujala, IKDC, and EQ-5D at one year postoperatively (p < 0.001) with a recognized level of overall complication rate. Conclusion. The OBC is a valid assessment tool to grade patients with trochlear dysplasia and, when used as part of the OPTA, helps to determine treatments that are safe and effective. This fulfils the requirements for its application in mainstream clinical practice. Cite this article: Bone Joint J 2021;103-B(10):1586–1594

Aims. Total hip arthroplasty (THA) in patients with post-polio residual paralysis (PPRP) is challenging. Despite relief in pain after THA, pre-existing muscle imbalance and altered gait may cause persistence of difficulty in walking. The associated soft tissue contractures not only imbalances the pelvis, but also poses the risk of dislocation, accelerated polyethylene liner wear, and early loosening. Methods. In all, ten hips in ten patients with PPRP with fixed pelvic obliquity who underwent THA as per an algorithmic approach in two centres from January 2014 to March 2018 were followed-up for a minimum of two years (2 to 6). All patients required one or more additional soft tissue procedures in a pre-determined sequence to correct the pelvic obliquity. All were invited for the latest clinical and radiological assessment. Results. The mean Harris Hip Score at the latest follow-up was 79.2 (68 to 90). There was significant improvement in the coronal pelvic obliquity from 16.6. o. (SD 7.9. o. ) to 1.8. o. (SD 2.4. o. ; p < 0.001). Radiographs of all ten hips showed stable prostheses with no signs of loosening or migration, regardless of whether paralytic or non-paralytic hip was replaced. No complications, including dislocation or infection related to the surgery, were observed in any patient. The subtrochanteric shortening osteotomy done in two patients had united by nine months. Conclusion. Simultaneous correction of soft tissue contractures is necessary for obtaining a stable hip with balanced pelvis while treating hip arthritis by THA in patients with PPRP and fixed pelvic obliquity. Cite this article: Bone Jt Open 2021;2(9):696–704

Aims. Fungal periprosthetic joint infections (fPJIs) are rare complications, constituting only 1% of all PJIs. Neither a uniform definition for fPJI has been established, nor a standardized treatment regimen. Compared to bacterial PJI, there is little evidence for fPJI in the literature with divergent results. Hence, we implemented a novel treatment algorithm based on three-stage revision arthroplasty, with local and systemic antifungal therapy to optimize treatment for fPJI. Methods. From 2015 to 2018, a total of 18 patients with fPJI were included in a prospective, single-centre study (DKRS-ID 00020409). The diagnosis of PJI is based on the European Bone and Joint Infection Society definition of periprosthetic joint infections. The baseline parameters (age, sex, and BMI) and additional data (previous surgeries, pathogen spectrum, and Charlson Comorbidity Index) were recorded. A therapy protocol with three-stage revision, including a scheduled spacer exchange, was implemented. Systemic antifungal medication was administered throughout the entire treatment period and continued for six months after reimplantation. A minimum follow-up of 24 months was defined. Results. Eradication of infection was achieved in 16 out of 18 patients (88.8%), with a mean follow-up of 35 months (25 to 54). Mixed bacterial and fungal infections were present in seven cases (39%). The interval period, defined as the period of time from explantation to reimplantation, was 119 days (55 to 202). In five patients, a salvage procedure was performed (three cementless modular knee arthrodesis, and two Girdlestone procedures). Conclusion. Therapy for fPJI is complex, with low cure rates according to the literature. No uniform treatment recommendations presently exist for fPJI. Three-stage revision arthroplasty with prolonged systemic antifungal therapy showed promising results. Cite this article: Bone Jt Open 2021;2(8):671–678

The use of plate-and-cable constructs to treat periprosthetic fractures around a well-fixed femoral component in total hip replacements has been reported to have high rates of failure. Our aim was to evaluate the results of a surgical treatment algorithm to use these lateral constructs reliably in Vancouver type-B1 and type-C fractures. The joint was dislocated and the stability of the femoral component was meticulously evaluated in 45 type-B1 fractures. This led to the identification of nine (20%) unstable components. The fracture was considered to be suitable for single plate-and-cable fixation by a direct reduction technique if the integrity of the medial cortex could be restored. Union was achieved in 29 of 30 fractures (97%) at a mean of 6.4 months (3 to 30) in 29 type-B1 and five type-C fractures. Three patients developed an infection and one construct failed. Using this algorithm plate-and-cable constructs can be used safely, but indirect reduction with minimal soft-tissue damage could lead to shorter times to union and lower rates of complications

The purpose of this study was to evaluate treatment results following arthroscopic triangular fibrocartilage complex (TFCC) debridement for recalcitrant ulnar wrist pain. According to the treatment algorithm, 66 patients (36 men and 30 women with a mean age of 38.1 years (15 to 67)) with recalcitrant ulnar wrist pain were allocated to undergo ulnar shortening osteotomy (USO; n = 24), arthroscopic TFCC repair (n = 15), arthroscopic TFCC debridement (n = 14) or prolonged conservative treatment (n = 13). The mean follow-up was 36.0 months (15 to 54). Significant differences in Hand20 score at 18 months were evident between the USO group and TFCC debridement group (p = 0.003), and between the TFCC repair group and TFCC debridement group (p = 0.029). Within-group comparisons showed that Hand20 score at five months or later and pain score at two months or later were significantly decreased in the USO/TFCC repair groups. In contrast, scores in the TFCC debridement/conservative groups did not decrease significantly. Grip strength at 18 months was significantly improved in the USO/TFCC repair groups, but not in the TFCC debridement/conservative groups. TFCC debridement shows little benefit on the clinical course of recalcitrant ulnar wrist pain even after excluding patients with ulnocarpal abutment or TFCC detachment from the fovea from the indications for arthroscopic TFCC debridement. Cite this article: Bone Joint J 2013;95-B:1687–96

Cite this article: Bone Joint Res 2023;12(9):598–600.

We investigated the incidence of anomalies in the vertebral arteries and Circle of Willis with three-dimensional CT angiography in 55 consecutive patients who had undergone an instrumented posterior fusion of the cervical spine.

We recorded any peri-operative and post-operative complications. The frequency of congenital anomalies was 30.9%, abnormal vertebral artery blood flow was 58.2% and vertebral artery dominance 40%.

The posterior communicating artery was occluded on one side in 41.8% of patients and bilaterally in 38.2%. Variations in the vertebral arteries and Circle of Willis were not significantly related to the presence or absence of posterior communicating arteries. Importantly, 18.2% of patients showed characteristic variations in the Circle of Willis with unilateral vertebral artery stenosis or a dominant vertebral artery, indicating that injury may cause lethal complications. One patient had post-operative cerebellar symptoms due to intra-operative injury of the vertebral artery, and one underwent a different surgical procedure because of insufficient collateral circulation.

Pre-operative assessment of the vertebral arteries and Circle of Willis is essential if a posterior spinal fusion with instrumentation is to be carried out safely.

Cite this article: Bone Joint J 2014;96-B:535–40.

Aims. An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. Methods. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data. Results. The patient-specific approach with engineered features achieved the highest in-clinic performance for differentiating physiotherapy exercise from non-exercise activity (area under the receiver operating characteristic (AUROC) = 0.924). Including non-exercise data in algorithm training further improved classifier performance (random forest, AUROC = 0.985). The highest accuracy achieved for classifying individual in-clinic exercises was 0.903, using a patient-specific method with deep neural network model extracted features. Grouping exercises by motion type improved exercise classification. For at-home data, OOD detection yielded similar performance with the non-exercise data in the algorithm training (fully convolutional network AUROC = 0.919). Conclusion. Including non-exercise data in algorithm training improves detection of exercises. A patient-specific approach leveraging data from earlier patient-supervised sessions should be considered but is highly dependent on per-patient data quality. Cite this article: Bone Joint Res 2023;12(3):165–177

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. 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. Our objective was describing an algorithm to identify and prevent vascular injury in patients with intrapelvic components. Methods. Patients were defined as at risk to vascular injuries when components or cement migrated 5 mm or more beyond the ilioischial line in any of the pelvic incidences (anteroposterior and Judet view). In those patients, a serial investigation was initiated by a CT angiography, followed by a vascular surgeon evaluation. The investigation proceeded if necessary. The main goal was to assure a safe tissue plane between the hardware and the vessels. Results. In ten at-risk patients undergoing revision hip arthroplasty and submitted to our algorithm, six were recognized as being high risk to vascular injury during surgery. In those six high-risk patients, a preventive preoperative stent was implanted before the orthopaedic procedure. Four patients needed a second reinforcing stent to protect and to maintain the vessel anatomy deformed by the intrapelvic implants. Conclusion. The evaluation algorithm was useful to avoid blood vessels injury during revision total hip arthroplasty in high-risk patients. Cite this article: Bone Jt Open 2022;3(11):859–866

Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles (. https://www.ideal-collaboration.net/. ). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams. Cite this article: Bone Joint Res 2024;13(9):507–512

The October 2023 Spine Roundup. 360. looks at: Cutting through surgical smoke: the science of cleaner air in spinal operations; Unlocking success: key factors in thoracic spine decompression and fusion for ossification of the posterior longitudinal ligament; Deep learning algorithm for identifying cervical cord compression due to degenerative canal stenosis on radiography; Surgeon experience influences robotics learning curve for minimally invasive lumbar fusion; Decision-making algorithm for the surgical treatment of degenerative lumbar spondylolisthesis of L4/L5; Response to preoperative steroid injections predicts surgical outcomes in patients undergoing fusion for isthmic spondylolisthesis

Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255

Aims. Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A DL workflow was created to measure the FMAA and validated against human measurements. To reflect potential intramedullary guide placement during manual TKA, two different FMAAs were calculated either using a line approximating the entire diaphyseal shaft, and a line connecting the apex of the femoral intercondylar sulcus to the centre of the diaphysis. The proportion of FMAAs outside a range of 5.0° (SD 2.0°) was calculated for both definitions, and FMAA was compared using univariate analyses across sex, BMI, knee alignment, and femur length. Results. The algorithm measured 1,078 radiographs at a rate of 12.6 s/image (2,156 unique measurements in 3.8 hours). There was no significant difference or bias between reader and algorithm measurements for the FMAA (p = 0.130 to 0.563). The FMAA was 6.3° (SD 1.0°; 25% outside range of 5.0° (SD 2.0°)) using definition one and 4.6° (SD 1.3°; 13% outside range of 5.0° (SD 2.0°)) using definition two. Differences between males and females were observed using definition two (males more valgus; p < 0.001). Conclusion. We developed a rapid and accurate DL tool to quantify the FMAA. Considerable variation with different measurement approaches for the FMAA supports that patient-specific anatomy and surgeon-dependent technique must be accounted for when correcting for the FMAA using an intramedullary guide. The angle between the mechanical and anatomical axes of the femur fell outside the range of 5.0° (SD 2.0°) for nearly a quarter of patients. Cite this article: Bone Jt Open 2024;5(2):101–108

Aims. This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. Results. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. Conclusion. The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets. Cite this article: Bone Joint Res 2024;13(2):66–82

Aims. Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. Methods. A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity). Results. The NLP algorithm performed well at extracting variables from unstructured data in our random test dataset (accuracy = 96.3%, sensitivity = 95.2%, and specificity = 97.4%). It performed better at extracting data that were in a structured, templated format such as range of movement (ROM) (accuracy = 98%) and implant brand (accuracy = 98%) than data that were entered with variation depending on the author of the note such as the presence of deep-vein thrombosis (DVT) (accuracy = 90%). Conclusion. The NLP algorithm used in this study was able to identify a subset of variables from randomly selected unstructured notes in arthroplasty with an accuracy above 90%. For some variables, such as objective exam data, the accuracy was very high. Our findings suggest that automated algorithms using NLP can help orthopaedic practices retrospectively collect information for registries and quality improvement (QI) efforts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):99–104

Aims. An algorithm to determine the constitutional alignment of the lower limb once arthritic deformity has occurred would be of value when undertaking kinematically aligned total knee arthroplasty (TKA). The purpose of this study was to determine if the arithmetic hip-knee-ankle angle (aHKA) algorithm could estimate the constitutional alignment of the lower limb following development of significant arthritis. Methods. A matched-pairs radiological study was undertaken comparing the aHKA of an osteoarthritic knee (aHKA-OA) with the mechanical HKA of the contralateral normal knee (mHKA-N). Patients with Grade 3 or 4 Kellgren-Lawrence tibiofemoral osteoarthritis in an arthritic knee undergoing TKA and Grade 0 or 1 osteoarthritis in the contralateral normal knee were included. The aHKA algorithm subtracts the lateral distal femoral angle (LDFA) from the medial proximal tibial angle (MPTA) measured on standing long leg radiographs. The primary outcome was the mean of the paired differences in the aHKA-OA and mHKA-N. Secondary outcomes included comparison of sex-based differences and capacity of the aHKA to determine the constitutional alignment based on degree of deformity. Results. A total of 51 radiographs met the inclusion criteria. There was no significant difference between aHKA-OA and mHKA-N, with a mean angular difference of −0.4° (95% SE −0.8° to 0.1°; p = 0.16). There was no significant sex-based difference when comparing aHKA-OA and mHKA-N (mean difference 0.8°; p = 0.11). Knees with deformities of more than 8° had a greater mean difference between aHKA-OA and mHKA-N (1.3°) than those with lesser deformities (-0.1°; p = 0.009). Conclusion. This study supports the arithmetic HKA algorithm for prediction of the constitutional alignment once arthritis has developed. The algorithm has similar accuracy between sexes and greater accuracy with lesser degrees of deformity. Cite this article: Bone Joint Open 2020;1-7:339–345

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article: Bone Joint Res 2023;12(7):447–454

The August 2024 Spine Roundup. 360. looks at: Laminectomy adjacent to instrumented fusion increases adjacent segment disease; Influence of the timing of surgery for cervical spinal cord injury without bone injury in the elderly: a retrospective multicentre study; Lumbar vertebral body tethering: single-centre outcomes and reoperations in a consecutive series of 106 patients; Machine-learning algorithms for predicting Cobb angle beyond 25° in female adolescent idiopathic scoliosis patients; Pain in adolescent idiopathic scoliosis; Teriparatide prevents surgery for osteoporotic vertebral compression fracture

Aims. Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors. Methods. This was an international, multicentre retrospective study of 1,174 revision total hip (THA) and knee arthroplasties (TKA) undergoing I&D for PJI between January 2005 and December 2017. PJI was defined using the Musculoskeletal Infection Society (MSIS) criteria. A total of 52 variables including demographics, comorbidities, and clinical and laboratory findings were evaluated using random forest machine learning analysis. The algorithm was then verified through cross-validation. Results. Of the 1,174 patients that were included in the study, 405 patients (34.5%) failed treatment. Using random forest analysis, an algorithm that provides the probability for failure for each specific patient was created. By order of importance, the ten most important variables associated with failure of I&D were serum CRP levels, positive blood cultures, indication for index arthroplasty other than osteoarthritis, not exchanging the modular components, use of immunosuppressive medication, late acute (haematogenous) infections, methicillin-resistant Staphylococcus aureus infection, overlying skin infection, polymicrobial infection, and older age. The algorithm had good discriminatory capability (area under the curve = 0.74). Cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. Conclusion. This is the first study in the orthopaedic literature to use machine learning as a tool for predicting outcomes following I&D surgery. The developed algorithm provides the medical profession with a tool that can be employed in clinical decision-making and improve patient care. Future studies should aid in further validating this tool on additional cohorts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):11–19

Aims. Custom-made partial pelvis replacements (PPRs) are increasingly used in the reconstruction of large acetabular defects and have mainly been designed using a triflange approach, requiring extensive soft-tissue dissection. The monoflange design, where primary intramedullary fixation within the ilium combined with a monoflange for rotational stability, was anticipated to overcome this obstacle. The aim of this study was to evaluate the design with regard to functional outcome, complications, and acetabular reconstruction. Methods. Between 2014 and 2023, 79 patients with a mean follow-up of 33 months (SD 22; 9 to 103) were included. Functional outcome was measured using the Harris Hip Score and EuroQol five-dimension questionnaire (EQ-5D). PPR revisions were defined as an endpoint, and subgroups were analyzed to determine risk factors. Results. Implantation was possible in all cases with a 2D centre of rotation deviation of 10 mm (SD 5.8; 1 to 29). PPR revision was necessary in eight (10%) patients. HHS increased significantly from 33 to 72 postoperatively, with a mean increase of 39 points (p < 0.001). Postoperative EQ-5D score was 0.7 (SD 0.3; -0.3 to 1). Risk factor analysis showed significant revision rates for septic indications (p ≤ 0.001) as well as femoral defect size (p = 0.001). Conclusion. Since large acetabular defects are being treated surgically more often, custom-made PPR should be integrated as an option in treatment algorithms. Monoflange PPR, with primary iliac fixation, offers a viable treatment option for Paprosky III defects with promising functional results, while requiring less soft-tissue exposure and allowing immediate full weightbearing. Cite this article: Bone Jt Open 2024;5(8):688–696

Aims. The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. Methods. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%). Results. For detection and classification, the algorithm was trained on 1,709 radiographs (n = 803), tested on 567 radiographs (n = 244), and subsequently externally validated on 535 radiographs (n = 227). For characterization, healthy shoulders and glenohumeral dislocation were excluded. The overall accuracy for fracture detection was 94% (area under the receiver operating characteristic curve (AUC) = 0.98) and for classification 78% (AUC 0.68 to 0.93). Accuracy to detect greater tuberosity fracture displacement ≥ 1 cm was 35.0% (AUC 0.57). The CNN did not recognize NSAs ≤ 100° (AUC 0.42), nor fractures with ≥ 75% shaft translation (AUC 0.51 to 0.53), or with ≥ 15% articular involvement (AUC 0.48 to 0.49). For all objectives, the model’s performance on the external dataset showed similar accuracy levels. Conclusion. CNNs proficiently rule out proximal humerus fractures on plain radiographs. Despite rigorous training methodology based on CT imaging with multi-rater consensus to serve as the reference standard, artificial intelligence-driven classification is insufficient for clinical implementation. The CNN exhibited poor diagnostic ability to detect greater tuberosity displacement ≥ 1 cm and failed to identify NSAs ≤ 100°, shaft translations, or articular fractures. Cite this article: Bone Joint J 2024;106-B(11):1348–1360

Aims. Obtaining solid implant fixation is crucial in revision total knee arthroplasty (rTKA) to avoid aseptic loosening, a major reason for re-revision. This study aims to validate a novel grading system that quantifies implant fixation across three anatomical zones (epiphysis, metaphysis, diaphysis). Methods. Based on pre-, intra-, and postoperative assessments, the novel grading system allocates a quantitative score (0, 0.5, or 1 point) for the quality of fixation achieved in each anatomical zone. The criteria used by the algorithm to assign the score include the bone quality, the size of the bone defect, and the type of fixation used. A consecutive cohort of 245 patients undergoing rTKA from 2012 to 2018 were evaluated using the current novel scoring system and followed prospectively. In addition, 100 first-time revision cases were assessed radiologically from the original cohort and graded by three observers to evaluate the intra- and inter-rater reliability of the novel radiological grading system. Results. At a mean follow-up of 90 months (64 to 130), only two out of 245 cases failed due to aseptic loosening. Intraoperative grading yielded mean scores of 1.87 (95% confidence interval (CI) 1.82 to 1.92) for the femur and 1.96 (95% CI 1.92 to 2.0) for the tibia. Only 3.7% of femoral and 1.7% of tibial reconstructions fell below the 1.5-point threshold, which included the two cases of aseptic loosening. Interobserver reliability for postoperative radiological grading was 0.97 for the femur and 0.85 for the tibia. Conclusion. A minimum score of 1.5 points for each skeletal segment appears to be a reasonable cut-off to define sufficient fixation in rTKA. There were no revisions for aseptic loosening at mid-term follow-up when this fixation threshold was achieved or exceeded. When assessing first-time revisions, this novel grading system has shown excellent intra- and interobserver reliability. Cite this article: Bone Joint J 2024;106-B(5):468–474

Aims. Accurate diagnosis of chronic periprosthetic joint infection (PJI) presents a significant challenge for hip surgeons. Preoperative diagnosis is not always easy to establish, making the intraoperative decision-making process crucial in deciding between one- and two-stage revision total hip arthroplasty (THA). Calprotectin is a promising point-of-care novel biomarker that has displayed high accuracy in detecting PJI. We aimed to evaluate the utility of intraoperative calprotectin lateral flow immunoassay (LFI) in THA patients with suspected chronic PJI. Methods. The study included 48 THAs in 48 patients with a clinical suspicion of PJI, but who did not meet European Bone and Joint Infection Society (EBJIS) PJI criteria preoperatively, out of 105 patients undergoing revision THA at our institution for possible PJI between November 2020 and December 2022. Intraoperatively, synovial fluid calprotectin was measured with LFI. Cases with calprotectin levels ≥ 50 mg/l were considered infected and treated with two-stage revision THA; in negative cases, one-stage revision was performed. At least five tissue cultures were obtained; the implants removed were sent for sonication. Results. Calprotectin was positive (≥ 50 mg/l) in 27 cases; out of these, 25 had positive tissue cultures and/or sonication. Calprotectin was negative in 21 cases. There was one false negative case, which had positive tissue cultures. Calprotectin showed an area under the curve of 0.917, sensitivity of 96.2%, specificity of 90.9%, positive predictive value of 92.6%, negative predictive value of 95.2%, positive likelihood ratio of 10.6, and negative likelihood ratio of 0.04. Overall, 45/48 patients were correctly diagnosed and treated by our algorithm, which included intraoperative calprotectin measurement. This yielded a 93.8% concordance with postoperatively assessed EBJIS criteria. Conclusion. Calprotectin can be a valuable tool in facilitating the intraoperative decision-making process for cases in which chronic PJI is suspected and diagnosis cannot be established preoperatively. Cite this article: Bone Joint J 2024;106-B(5 Supple B):118–124

Aims. The management of fractures of the medial epicondyle is one of the greatest controversies in paediatric fracture care, with uncertainty concerning the need for surgery. The British Society of Children’s Orthopaedic Surgery prioritized this as their most important research question in paediatric trauma. This is the protocol for a randomized controlled, multicentre, prospective superiority trial of operative fixation versus nonoperative treatment for displaced medial epicondyle fractures: the Surgery or Cast of the EpicoNdyle in Children’s Elbows (SCIENCE) trial. Methods. Children aged seven to 15 years old inclusive, who have sustained a displaced fracture of the medial epicondyle, are eligible to take part. Baseline function using the Patient-Reported Outcomes Measurement Information System (PROMIS) upper limb score, pain measured using the Wong Baker FACES pain scale, and quality of life (QoL) assessed with the EuroQol five-dimension questionnaire for younger patients (EQ-5D-Y) will be collected. Each patient will be randomly allocated (1:1, stratified using a minimization algorithm by centre and initial elbow dislocation status (i.e. dislocated or not-dislocated at presentation to the emergency department)) to either a regimen of the operative fixation or non-surgical treatment. Outcomes. At six weeks, and three, six, and 12 months, data on function, pain, sports/music participation, QoL, immobilization, and analgesia will be collected. These will also be repeated annually until the child reaches the age of 16 years. Four weeks after injury, the main outcomes plus data on complications, resource use, and school absence will be collected. The primary outcome is the PROMIS upper limb score at 12 months post-randomization. All data will be obtained through electronic questionnaires completed by the participants and/or parents/guardians. The NHS number of participants will be stored to enable future data linkage to sources of routinely collected data (i.e. Hospital Episode Statistics). Cite this article: Bone Jt Open 2024;5(1):69–77

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. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407

Aims. To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Methods. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aims. Ganz’s studies made it possible to address joint deformities on both the femoral and acetabular side brought about by Perthes’ disease. Femoral head reduction osteotomy (FHRO) was developed to improve joint congruency, along with periacetabular osteotomy (PAO), which may enhance coverage and containment. The purpose of this study is to show the clinical and morphological outcomes of the technique and the use of an implemented planning approach. Methods. From September 2015 to December 2021, 13 FHROs were performed on 11 patients for Perthes’ disease in two centres. Of these, 11 hips had an associated PAO. A specific CT- and MRI-based protocol for virtual simulation of the corrections was developed. Outcomes were assessed with radiological parameters (sphericity index, extrusion index, integrity of the Shenton’s line, lateral centre-edge angle (LCEA), Tönnis angle), and clinical parameters (range of motion, visual analogue scale (VAS) for pain, Merle d'Aubigné-Postel score, modified Harris Hip Score (mHHS), and EuroQol five-dimension five-level health questionnaire (EQ-5D-5L)). Early and late complications were reported. Results. The mean follow-up was 39.7 months (standard deviation (SD) 26.4). The mean age at surgery was 11.4 years (SD 1.6). No major complications were recorded. One patient required a total hip arthroplasty. Mean femoral head sphericity increased from 46.8% (SD 9.34%) to 70.2% (SD 15.44; p < 0.001); mean LCEA from 19.2° (SD 9.03°) to 44° (SD 10.27°; p < 0.001); mean extrusion index from 37.8 (SD 8.70) to 7.5 (SD 9.28; p < 0.001); and mean Tönnis angle from 16.5° (SD 12.35°) to 4.8° (SD 4.05°; p = 0.100). The mean VAS improved from 3.55 (SD 3.05) to 1.22 (1.72; p = 0.06); mean Merle d’Aubigné-Postel score from 14.55 (SD 1.74) to 16 (SD 1.6; p = 0.01); and mean mHHS from 60.6 (SD 18.06) to 81 (SD 6.63; p = 0.021). The EQ-5D-5L also showed significant improvements. Conclusion. FHRO associated with periacetabular procedures is a safe technique that showed improved functional, clinical, and morphological outcomes in Perthes’ disease. The newly introduced simulation and planning algorithm may help to further refine the technique. Cite this article: Bone Joint J 2024;106-B(5 Supple B):40–46

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. Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results. Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion. These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937

Aims. This study aimed, through bioinformatics analysis, to identify the potential diagnostic markers of osteoarthritis, and analyze the role of immune infiltration in synovial tissue. Methods. The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by R software. Functional enrichment analyses were performed and protein-protein interaction networks (PPI) were constructed. Then the hub genes were screened. Biomarkers with high value for the diagnosis of early osteoarthritis (OA) were validated by GEO datasets. Finally, the CIBERSORT algorithm was used to evaluate the immune infiltration between early-stage OA and end-stage OA, and the correlation between the diagnostic marker and infiltrating immune cells was analyzed. Results. A total of 88 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that DEGs were significantly enriched in leucocyte migration and interleukin (IL)-17 signalling pathways. Disease ontology (DO) indicated that DEGs were mostly enriched in rheumatoid arthritis. Six hub genes including FosB proto-oncogene, AP-1 transcription factor subunit (FOSB); C-X-C motif chemokine ligand 2 (CXCL2); CXCL8; IL-6; Jun proto-oncogene, AP-1 transcription factor subunit (JUN); and Activating transcription factor 3 (ATF3) were identified and verified by GEO datasets. ATF3 (area under the curve = 0.975) turned out to be a potential biomarker for the diagnosis of early OA. Several infiltrating immune cells varied significantly between early-stage OA and end-stage OA, such as resting NK cells (p = 0.016), resting dendritic cells (p = 0.043), and plasma cells (p = 0.043). Additionally, ATF3 was significantly correlated with resting NK cells (p = 0.034), resting dendritic cells (p = 0.026), and regulatory T cells (Tregs, p = 0.018). Conclusion. ATF3 may be a potential diagnostic marker for early diagnosis and treatment of OA, and immune cell infiltration provides new perspectives for understanding the mechanism during OA progression. Cite this article: Bone Joint Res 2022;11(9):679–689

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article: Bone Joint J 2021;103-B(9):1442–1448

Aims. This study aimed to develop a virtual clinic for the purpose of reducing face-to-face orthopaedic consultations. Patients and Methods. Anonymized experts (hip and knee arthroplasty patients, surgeons, physiotherapists, radiologists, and arthroplasty practitioners) gave feedback via a Delphi Consensus Technique. This consisted of an iterative sequence of online surveys, during which virtual documents, made up of a patient-reported questionnaire, standardized radiology report, and decision-guiding algorithm, were modified until consensus was achieved. We tested the patient-reported questionnaire on seven patients in orthopaedic clinics using a ‘think-aloud’ process to capture difficulties with its completion. Results. A patient-reported 13-item questionnaire was developed covering pain, mobility, and activity. The radiology report included up to ten items (e.g. progressive periprosthetic bone loss) depending on the type of arthroplasty. The algorithm concludes in one of three outcomes: review at surgeon’s discretion (three to 12 months); see at next available clinic; or long-term follow-up/discharge. Conclusion. The virtual clinic approach with attendant documents achieved consensus by orthopaedic experts, radiologists, and patients. The robust development and testing of this standardized virtual clinic provided a sound platform for organizations in the United Kingdom to adopt a virtual clinic approach for follow-up of hip and knee arthroplasty patients. Cite this article: Bone Joint J 2019;101-B:951–959

Aims. This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Methods. Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay. Results. The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis. Conclusion. Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358–1366

Aims. The aim of this study was to determine the impact of hospital-level service characteristics on hip fracture outcomes and quality of care processes measures. Methods. This was a retrospective analysis of publicly available audit data obtained from the National Hip Fracture Database (NHFD) 2018 benchmark summary and Facilities Survey. Data extraction was performed using a dedicated proforma to identify relevant hospital-level care process and outcome variables for inclusion. The primary outcome measure was adjusted 30-day mortality rate. A random forest-based multivariate imputation by chained equation (MICE) algorithm was used for missing value imputation. Univariable analysis for each hospital level factor was performed using a combination of Tobit regression, Siegal non-parametric linear regression, and Mann-Whitney U test analyses, dependent on the data type. In all analyses, a p-value < 0.05 denoted statistical significance. Results. Analyses included 176 hospitals, with a median of 366 hip fracture cases per year (interquartile range (IQR) 280 to 457). Aggregated data from 66,578 patients were included. The only identified hospital-level variable associated with the primary outcome of 30-day mortality was hip fracture trial involvement (no trial involvement: median 6.3%; trial involvement: median 5.7%; p = 0.039). Significant key associations were also identified between prompt surgery and presence of dedicated hip fracture sessions; reduced acute length of stay and both a higher number of hip fracture cases per year and more dedicated hip fracture operating lists; Best Practice Tariff attainment and greater number of hip fracture cases per year, more dedicated hip fracture operating lists, presence of a dedicated hip fracture ward, and hip fracture trial involvement. Conclusion. Exploratory analyses have identified that improved outcomes in hip fracture may be associated with hospital-level service characteristics, such as hip fracture research trial involvement, larger hip fracture volumes, and the use of theatre lists dedicated to hip fracture surgery. Further research using patient level data is warranted to corroborate these findings. Cite this article: Bone Joint J 2021;103-B(10):1627–1632

Aims. Current guidelines consider analyses of joint aspirates, including leucocyte cell count (LC) and polymorphonuclear percentage (PMN%) as a diagnostic mainstay of periprosthetic joint infection (PJI). It is unclear if these parameters are subject to a certain degree of variability over time. Therefore, the aim of this study was to evaluate the variation of LC and PMN% in patients with aseptic revision total knee arthroplasty (TKA). Methods. We conducted a prospective, double-centre study of 40 patients with 40 knee joints. Patients underwent joint aspiration at two different time points with a maximum period of 120 days in between these interventions and without any events such as other joint aspirations or surgeries. The main indications for TKA revision surgery were aseptic implant loosening (n = 24) and joint instability (n = 11). Results. Overall, 80 synovial fluid samples of 40 patients were analyzed. The average time period between the joint aspirations was 50 days (SD 32). There was a significantly higher percentage change in LC when compared to PMN% (44.1% (SD 28.6%) vs 27.3% (SD 23.7%); p = 0.003). When applying standard definition criteria, LC counts were found to skip back and forth between the two time points with exceeding the thresholds in up to 20% of cases, which was significantly more compared to PMN% for the European Bone and Joint Infection Society (EBJIS) criteria (p = 0.001), as well as for Musculoskeletal Infection Society (MSIS) (p = 0.029). Conclusion. LC and PMN% are subject to considerable variation. According to its higher interindividual variance, LC evaluation might contribute to false-positive or false-negative results in PJI assessment. Single LC testing prior to TKA revision surgery seems to be insufficient to exclude PJI. On the basis of the obtained results, PMN% analyses overrule LC measurements with regard to a conclusive diagnostic algorithm. Cite this article: Bone Jt Open 2021;2(8):566–572