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. 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. 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

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

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

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. 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. While previously underappreciated, factors related to the spine contribute substantially to the risk of dislocation following total hip arthroplasty (THA). These factors must be taken into consideration during preoperative planning for revision THA due to recurrent instability. We developed a protocol to assess the functional position of the spine, the significance of these findings, and how to address different pathologies at the time of revision THA. Patients and Methods. Prospectively collected data on 111 patients undergoing revision THA for recurrent instability from January 2014 to January 2017 at two institutions were included (protocol group) and matched 1:1 to 111 revisions specifically performed for instability not using this protocol (control group). Mean follow-up was 2.8 years. Protocol patients underwent standardized preoperative imaging including supine and standing anteroposterior (AP) pelvis and lateral radiographs. Each case was scored according to the Hip-Spine Classification in Revision THA. Results. Survival free of dislocation at two years was 97% in the protocol group (three dislocations, all within three months of surgery) versus 84% in the control group (18 patients). Furthermore, 77% of the inappropriately positioned acetabular components would have been unrecognized by supine AP pelvis imaging alone. Conclusion. Using the Hip-Spine Classification System in revision THA, we demonstrated a significant decrease in the risk of recurrent instability compared with a control group. Without the use of this algorithm, 77% of inappropriately positioned acetabular components would have been unrecognized and incorrect treatment may have been instituted. Cite this article: Bone Joint J 2019;101-B:817–823

The early failure and revision of bimodular primary total hip arthroplasty prostheses requires the identification of the risk factors for material loss and wear at the taper junctions through taper wear analysis. Deviations in taper geometries between revised and pristine modular neck tapers were determined using high resolution tactile measurements. A new algorithm was developed and validated to allow the quantitative analysis of material loss, complementing the standard visual inspection currently used. The algorithm was applied to a sample of 27 retrievals (in situ from 2.9 to 38.1 months) of the withdrawn Rejuvenate modular prosthesis. The mean wear volumes on the flat distal neck piece taper was 3.35 mm. 3. (0.55 to 7.57), mainly occurring in a characteristic pattern in areas with high mechanical loading. Wear volume tended to increase with time to revision (r² = 0.423, p = 0.001). Implant and patient specific data (offset, stem size, patient’s mass, age and body mass index) did not correlate with the amount of material loss observed (p >  0.078). Bilaterally revised implants showed higher amounts of combined total material loss and similar wear patterns on both sides. The consistent wear pattern found in this study has not been reported previously, suggesting that the device design and materials are associated with the failure of this prosthesis. Cite this article: Bone Joint J 2015;97-B:1350–7

Aims

Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD. An artificial intelligence (AI) software named HIPPO automatically locates anatomical landmarks on anteroposterior pelvis radiographs and performs the needed measurements. The primary aim of this study was to assess the reliability of this tool as compared to multi-reader evaluation in clinically proven cases of adult HD. The secondary aims were to assess the time savings achieved and evaluate inter-reader assessment.

Persistent groin pain after seemingly successful total hip replacement (THR) appears to have become more common. Recent studies have indicated a high incidence after metal-on-polyethylene and metal-on-metal conventional THR and it has been documented in up to 18% of patients after metal-on-metal resurfacing. There are many causes, including acetabular loosening, stress fracture, and iliopsoas tendonitis and impingement. The evaluation of this problem requires a careful history and examination, plain radiographs and an algorithmic approach to special diagnostic imaging and tests. Non-operative treatment is not usually successful. Specific operative treatment depending on the cause of the pain usually involves revision of the acetabular component, iliopsoas tenotomy or other procedures, and is usually successful. Here, an appropriate algorithm is described

Aims

Cemented hemiarthroplasty is an effective form of treatment for most patients with an intracapsular fracture of the hip. However, it remains unclear whether there are subgroups of patients who may benefit from the alternative operation of a modern uncemented hemiarthroplasty – the aim of this study was to investigate this issue. Knowledge about the heterogeneity of treatment effects is important for surgeons in order to target operations towards specific subgroups who would benefit the most.

Aims

Iliopsoas impingement occurs in 4% to 30% of patients after undergoing total hip arthroplasty (THA). Despite a relatively high incidence, there are few attempts at modelling impingement between the iliopsoas and acetabular component, and no attempts at modelling this in a representative cohort of subjects. The purpose of this study was to develop a novel computational model for quantifying the impingement between the iliopsoas and acetabular component and validate its utility in a case-controlled investigation.

Aims

It is important to analyze objectively the hammering sound in cup press-fit technique in total hip arthroplasty (THA) in order to better understand the change of the sound during impaction. We hypothesized that a specific characteristic would present in a hammering sound with successful fixation. We designed the study to quantitatively investigate the acoustic characteristics during cementless cup impaction in THA.

Aims

It is important to analyze objectively the hammering sound in cup press-fit technique in total hip arthroplasty (THA) in order to better understand the change of the sound during impaction. We hypothesized that a specific characteristic would present in a hammering sound with successful fixation. We designed the study to quantitatively investigate the acoustic characteristics during cementless cup impaction in THA.

Aims

Spinopelvic pathology increases the risk for instability following total hip arthroplasty (THA), yet few studies have evaluated how pathology varies with age or sex. The aims of this study were: 1) to report differences in spinopelvic parameters with advancing age and between the sexes; and 2) to determine variation in the prevalence of THA instability risk factors with advancing age.

Aims

Femoroacetabular impingement (FAI) patients report exacerbation of hip pain in deep flexion. However, the exact impingement location in deep flexion is unknown. The aim was to investigate impingement-free maximal flexion, impingement location, and if cam deformity causes hip impingement in flexion in FAI patients.