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

Dislocation following revision THA remains a leading cause of failure. Integrity of the abductor muscles is a major contributor to stability. Large diameter heads (LDH), Dual Mobility (DM) and Constrained Acetabular Liners (CAL) are enhanced stability options but the indication for these choices remains unclear. We assessed an algorithm based on Gluteus Medius (GM) deficiency to determine bearing selection. Default choice with no GM damage was a LDH. GM deficiency with posterior muscle intact received DM and CAL for GM complete deficiency with loss of posterior muscle. Consecutive revision THA series followed to determine dislocation, all-cause re-revision and Oxford Hip Score (OHS). 311 revision THA with mean age 70 years (32–95). At a mean follow-up of 4.8 years overall dislocation rate 4.1% (95%CI 2.4–7.0) and survivorship free of re-revision 94.2% (95%CI 96.3–91.0). Outcomes:. Group 1 - LDH (36 & 40mm) n=164 / 4 dislocations / 7 re-revisions. Group 2 - DM n=73 / 3 dislocations / 4 re-revisions. Group 3 - CAL n=58 / 5 dislocations / 7 re-revisions. Group 4 - Other (28 & 32mm) n=16 / 1 dislocation / no re-revisions. Mean pre-op OHS: 19.6 (2–47) and mean post-op OHS: 33.9 (4–48). Kaplan-Meier analysis at 60 months dislocation-free survival was 96.1% (95% CI: 93.0–97.8). There was no difference between survival distributions comparing bearing choice (p=0.46). Decision making tools to guide selection are limited and in addition soft tissue deficiency has been poorly defined. The posterior vertical fibres of GM have the greatest lateral stabiliser effect on the hip. The algorithm we have used clearly defined indication & implant selection. We believe our outcomes support the use of an enhanced stability bearing selection algorithm

Aim. Treatment recommendations for periprosthetic joint infections (PJI) include surgical debridement, antibiotic therapy or staged revision. In surgical related foot and ankle infections (SR-FAI), implant removal will lead to instability. Debridement is difficult because the implant is outside the joint. Recommendations regarding PJI treatment can therefore not be extrapolated to the treatment of SR-FAI. Method. We searched PubMed for the etiology and treatment of SR-FAI, taken into account the time of occurrence, causative microorganisms and surgical treatment options. We integrated this knowledge into a treatment algorithm for SR-FAI. Results. Within the first 6 weeks after surgery, it is difficult to distinguish acute osteomyelitis from surgical site infection in which infection is limited to the soft tissue. The predominantly causative microorganism is Staphylococcus aureus. No debridement can be performed, because of the diffuse soft tissue inflammation and the absence of a joint space. If early SR- FAI is suspected without signs of systemic symptoms, fistula or abscess, empirical antibiotic treatment covering Staphylococcus aureus is recommended. If there is suspicion of ongoing SR-FAI after 2 weeks of empirical treatment, samples for culture after an antibiotic free window should be obtained to identify the causative microorganisms. If SR-FAI is confirmed, but there is no consolidation yet, targeted antibiotic treatment is given for 12 weeks without initial implant removal. In all other cases, debridement and samples for culture should be obtained after an antibiotic free window. Staged revision surgery will be performed if there is still a nonunion. Conclusions. Treatment algorithm regarding PJI cannot be extrapolated to the treatment of SR-FAI. Until now, no treatment guideline for SR-FAI is available. We have introduced a treatment algorithm for the treatment of SR-FAI. The guideline will be validated during the next 2 years

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

External validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Recently, machine learning was used to develop a tool that can quantify revision risk for a patient undergoing primary anterior cruciate ligament (ACL) reconstruction (https://swastvedt.shinyapps.io/calculator_rev/). The source of data included nearly 25,000 patients with primary ACL reconstruction recorded in the Norwegian Knee Ligament Register (NKLR). The result was a well-calibrated tool capable of predicting revision risk one, two, and five years after primary ACL reconstruction with moderate accuracy. The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR). The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For the index study, 24 total predictor variables in the NKLR were included and the models eliminated variables which did not significantly improve prediction ability - without sacrificing accuracy. The result was a well calibrated algorithm developed using the Cox Lasso model that only required five variables (out of the original 24) for outcome prediction. For this external validation study, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables were: graft choice, femur fixation device, Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life subscale score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration. In total, 10,922 DKLR patients were included for analysis. Average follow-up time or time-to-revision was 8.4 (±4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68-0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years. The NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in-clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre-operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown

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

Muller-Weiss disease is an uncommon condition with unclear etiology and no gold standard treatment. The question arises; which joints to fuse? Although no consensuses prevail, one must postulate fusion should include those affected. Consequently, to establish an algorithm for its surgical management we set out to study clinical and radiographic features with use of SPECT-CT and a literature review. 57 consecutive feet presenting with Muller-Weiss disease analysed; 15 men, 25 women, age 22–84. Condition bilateral in 17, left side 16, right in 7 patients. Specific history and examination by senior author. Radiographic series and SPECT-CT obtained with surgery performed on significantly symptomatic feet. Measurements of Meary-Tomeno angles, anteroposterior thickness of navicular at the midpoint of each naviculo-cuneiform, alongside the medial extrusion distance and percentage of compression in each case performed. Poor correlation between Meary's angle and 1) degree of compression at naviculo-cuneiform joints, 2) degree of extrusion 3) compression vs extrusion using R. 2. coefficient of determination (invalidating Maceira et al. classification). In unilateral cases, extrusion significantly greater on affected side 94.7% (P< 0.001 Fisher exact test). Degree of extrusion significantly greater in bilateral than unilateral cases (p=0.004 unpaired T test). Valgus hindfoot and Meary's negative most common pattern with no correlation between heel alignment and Meary's R. 2. = 0.003. SPECT-CT useful to determine subtalar involvement in ‘stage 2 disease.’. Following review of cases and published literature we propose the following classification for Muller-Weiss disease with treatment algorithm. 3 Stage delineation; Stage 1 (Normal hindfoot alignment); 1A. Talonavicular disease only - Isolated Talonavicular arthrodesis 1B. Talonavicular + Subtalar; double medial or triple arthrodesis. Stage 2. Talonavicular + Naviculocuneiform; 2A. Adequate bone stock - Talo-naviculo-cuneiform arthrodesis, 2B. Inadequate bone stock +- subtalar disease; Talo-naviculo-cuneiform arthrodesis with tricortical bone graft (Mayich). Stage 3; Asymmetric ankle varus. Pantalar arthrodesis Double/triple/TNC/TAR arthrodesis with hindfoot re-alignment

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

The Paprosky acetabular bone defect classification system and related algorithms for acetabular reconstruction cannot properly guide cementless acetabular reconstruction in the presence of porous metal augments. We aimed to introduce a rim, points, and column (RPC)-oriented cementless acetabular reconstruction algorithm and its clinical and radiographic outcomes. A total of 123 patients (128 hips) were enrolled. A minimum 5-year radiographic follow-up was available for 96 (75.8%) hips. The mean clinical and radiographic follow-up durations were 6.8±0.9 (range: 5.2–9.2) and 6.3±1.9 (range: 5.0–9.2) years, respectively. Harris hip score (HHS) improved significantly from 35.39±9.91 preoperatively to 85.98±12.81 postoperatively (P<0.001). Among the fixation modes, 42 (32.8%) hips were reconstructed with rim fixation, 42 (32.8%) with three-point fixation without point reconstruction, 40 (31.3%) with three-point fixation combined with point reconstruction, and 4 (3.1%) with three-point fixation combined with pelvic distraction. Complementary medial wall reconstruction was performed in 20 (15.6%) patients. All acetabular components were radiographically stable. Nine-year cumulative Kaplan–Meier survival rates for 123 patients with the endpoint defined as periprosthetic joint infection, any reoperation, and dissatisfaction were 96.91% (confidence interval [CI]: 86.26%, 99.34%), 97.66% (CI: 92.91%, 99.24%), and 96.06% (CI: 86.4%, 98.89%), respectively. Cup stability in cementless acetabular reconstruction depends on rim or three-point fixation. The continuity of the anterior and posterior columns determines whether the points provide adequate stability to the cup. Medial wall reconstruction is an important complementary fixation method for rim or three-point fixation. The patients who underwent cementless acetabular reconstruction guided by the RPC decision-making algorithm demonstrated satisfactory mid-term clinical function, satisfaction levels, radiographic results, and complication rates

Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation. Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV. Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective. Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education

Introduction. Instability remains a common complication following total hip arthroplasty (THA) and continues to account for the highest percentage of revisions in numerous registries. Many risk factors have been described, yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to apply a machine learning algorithm to develop a patient-specific risk score capable of dynamic adjustment based on operative decisions. Methods. 22,086 THA performed between 1998–2018 were evaluated. 632 THA sustained a postoperative dislocation (2.9%). Patients were robustly characterized based on non-modifiable factors: demographics, THA indication, spinal disease, spine surgery, neurologic disease, connective tissue disease; and modifiable operative decisions: surgical approach, femoral head size, acetabular liner (standard/elevated/constrained/dual-mobility). Models were built with a binary outcome (event/no event) at 1-year and 5-year postoperatively. Inverse Probability Censoring Weighting accounted for censoring bias. An ensemble algorithm was created that included Generalized Linear Model, Generalized Additive Model, Lasso Penalized Regression, Kernel-Based Support Vector Machines, Random Forest and Optimized Gradient Boosting Machine. Convex combination of weights minimized the negative binomial log-likelihood loss function. Ten-fold cross-validation accounted for the rarity of dislocation events. Results. The 1-year model achieved an area under the curve (AUC)=0.63, sensitivity=70%, specificity=50%, positive predictive value (PPV)=3% and negative predictive value (NPV)=99%. The 5-year model achieved an AUC=0.62, sensitivity=69%, specificity=51%, PPV=7% and NPV=97%. All cohort-level accuracy metrics performed better than chance. The two most influential predictors in the model were surgical approach and acetabular liner. Conclusions. This machine learning algorithm demonstrates high sensitivity and NPV, suggesting screening tool utility. The model is strengthened by a multivariable dataset portending differential dislocation risk. Two modifiable variables (approach and acetabular liner) were the most influential in dislocation risk. Calculator utilization in “app” form could enable individualized risk prognostication. Furthermore, algorithm development through machine learning facilitates perpetual model performance enhancement with future data input

Concepts in glenoid tracking and treatment strategies of glenoid bone loss are well established. Initial observations in our practice in Singapore showed few patients with major bone loss requiring glenoid reconstructions. This led us to investigate the incidence of and the extent of bone loss in our patients with shoulder instability. Our study revealed bony Bankart lesions were seen in 46% of our patients but glenoid bone loss measured only 6–10% of the glenoid surface. In the same study we found that arthroscopic labral repair with capsular plication and Mason-Ellen suturing (Hybrid technique) was sufficient to stabilise patients with bipolar bone defects and minor glenoid bone loss. This led us to develop the concept of minor bone loss and a new algorithm. Our algorithm and strategies to deal with major bone loss will also be discussed, and techniques & outcomes of Arthroscopic Bony Bankart repair, Arthroscopic Glenoid Reconstruction and Arthroscopic Remplissage procedures will be shown

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

Partial meniscectomy patients have a greater likelihood for the development of early osteoarthritis (OA). To prevent the onset of early OA, patient-specific treatment algorithms need to be created that predict patient risk to early OA after meniscectomy. The aim of this work was to identify patient-specific risk factors in partial meniscectomy patients that could potentially lead to early OA. Partial meniscectomy patients operated between 01/2017 and 12/2019 were evaluated in the study (n=317). Exclusion criteria were other pathologies or surgeries for the evaluated knee and meniscus (n = 114). Following informed consent, an online questionnaire containing demographics and the “Knee Injury and Osteoarthritis Outcome Score” (KOOS) questionnaire was sent to the patient. Based on the KOOS pain score, patients were classified into “low” (> 75) and “high” (< 75) risk patients, indicating risk to symptomatic OA. The “high risk” patients also underwent a follow-up including an MRI scan to understand whether they have developed early OA. From 203 participants, 96 patients responded to the questionnaire (116 did not respond) with 61 patients considered “low-risk” and 35 “high-risk” patients. Groups that showed a significant increased risk for OA were patients aged > 40 years, females, overweight (BMI >25 kg/m2 ≤ 30 kg/m2), and smokers (*p < 0.05). The “high-risk”-follow-up revealed a progression of early osteoarthritic cartilage changes in seven patients, with the remaining nineteen patients showing no changes in cartilage status or pain since time of operation. Additionally, eighteen patients in the high-risk group showed a varus or valgus axis deviation. Patient-specific factors for worse postoperative outcomes after partial meniscectomy and indicators for an “early OA” development were identified, providing the basis for a patient-specific treatment approach. Further analysis in a multicentre study and computational analysis of MRI scans is ongoing to develop a patient-specific treatment algorithm for meniscectomy patients

Introduction and Objective. Despite the low incidence of pilon fractures among lower limb injuries, their high-impact nature presents difficulties in surgical management and recovery. Current literature includes a wide range of different management strategies, however there is no universal treatment algorithm. We aim to determine clinical outcomes in patients with open and closed pilon fractures, managed using a treatment algorithm that was applied consistently over the span of this study. Materials and Methods. This retrospective study was conducted at a single institution, including 141 pilon fractures in 135 patients, from August 2014 to January 2021. AO/OTA classification was used to classify fractures. Among closed fractures, 12 had type 43A, 18 had type 43B, 61 had type 43C. Among open fractures, 11 had type 43A, 12 had type 43B, 27 had type 43C. Open fractures were further classified with Gustilo-Anderson (GA); type 1: n=8, type 2: n=10, type 3A: n=12, type 3B: n=20. Our treatment algorithm consisted of fine wire fixator (FWF) for severely comminuted closed fractures (AO/OTA type 43C3), or open fractures with severe soft tissue injury (GA type 3). Otherwise, open reduction internal fixation (ORIF) was performed. When required, minimally invasive osteosynthesis (MIO) was performed in combination with FWF to improve joint congruency. All open fractures, and closed fractures with severe soft tissue injury (skin contusion, fracture blister, severe oedema) were initially treated with temporary ankle-spanning external fixation. For all open fracture patients, surgical debridement, soft tissue cover with a free or pedicled flap were performed. For GA types 1 and 2, this was done with ORIF in the same operating session. Those with severe soft tissue injury (GA type 3) were treated with FWF four to six weeks after soft tissue management was completed. Primary outcome was AOFAS Ankle-Hindfoot score at 3, 6 and 12-months post-treatment. Secondary outcomes include time to partial weight-bear (PWB) and full weight-bear (FWB), bone union time. All complications were recorded. Results. Mean AOFAS score 3, 6, and 12 months post-treatment for open and closed fracture patients were 44.12 and 53.99 (p=0.007), 62.38 and 67.68 (p=0.203), 78.44 and 84.06 (p=0.256), respectively. 119 of the 141 fractures healed without further intervention (84.4%). Average time to bone union was 51.46 and 36.48 weeks for open and closed fractures, respectively (p=0.019). Union took longer in closed fracture patients treated with FWF than ORIF (p=0.025). On average, open and closed fracture patients took 12.29 and 10.76 weeks to PWB (p=0.361); 24.04 and 20.31 weeks to FWB (p=0.235), respectively. Common complications for open fractures were non-union (24%), post-traumatic arthritis (16%); for closed fractures they were post-traumatic arthritis (25%), superficial infection (22%). Open fracture was a risk factor for non-union (p=0.042; OR=2.558, 95% CI 1.016–6.441), bone defect (p=0.001; OR=5.973, 95% CI 1.986–17.967), and superficial infection (p<0.001; OR=4.167, 95% CI 1.978–8.781). Conclusions. The use of a two-staged approach involving temporary external fixation followed by definitive fixation, provides a stable milieu for soft tissue recovery. FWF combined with MIO, where required for severely comminuted closed fractures, and FWF for open fractures with severe soft tissue injury, are safe methods achieving low complication rates and good functional recovery

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