Increasing the accuracy of information provided through X-Rays maximises pre-operative planning. Aim of this project is to determine the necessity of calibration probes that would improve the accuracy of pre-operative templating. This is a retrospective study involving leg length and pelvis X-Rays performed across the NHS Lanarkshire from 01/03/2023 until 31/04/2024. A total of 87 leg length X-Rays were identified, 18 had a calibration probe present. Leg length was measured on each and the X-Rays were calibrated against the existing probe. In 66.7% of cases there was a major leg length discrepancy of over 2cm between the pre-calibrated and post-calibrated X-Rays. Pelvic X-Rays of 80 patients that underwent total hip replacement were reviewed. Preoperative templating was compared to the implants inserted. An average of 1.94 discrepancy in the size of the acetabular implant was identified whilst in 30 cases the size of the femoral stem was incorrect by at least 1 size. Magnification of 119.7% on X-Rays was found to provide the most accurate templating. Seventy seven cases of pelvic X-Rays before and after hip hemiarthroplasty were also reviewed. The implant head was templated incorrectly in 74% of cases and the stem in 51%. It was identified that pelvic X-Ray magnification of 121.7% would provide the most accurate results. X-Rays with no calibration probes provide inaccurate measurements leading to faulty preoperative planning. Standardised use of a calibration sphere is strongly suggested and whenever that is not available, we suggest magnification of 121%

Neck of femur fractures are a common presentation and certain patients can be managed with a total hip replacement. To receive a total hip replacement the pelvic X-rays should be templated as per AO guidelines and a common way this is performed is by including a calibration marker on the X-ray. The aim of this study is to assess and improve upon the use of the calibration marker. Details of patients admitted with a neck of femur fracture from January 1st 2018 until December 31st 2018 were gathered and used to review each initial X-ray and determine if a calibration marker was included. 376 patients were admitted with a neck of femur fracture over the one year period. 36% of patients did not have a calibration marker on their initial pelvic X-ray and 11% did not have a chest X ray. 215 patients had an intracapsular fracture and 39 went on to have a total hip replacement. 12 patients were lacking a calibration marker on their original X ray and required a repeat X ray. After a poster was placed in the radiographer booth acting as a visual aid, the use of a calibration marker improved from 62% to 70%. Calibration markers are useful tools which can aid the pre-operative planning for hip replacement surgeries shortening operative time, increase precision and reduce prosthetic loosening, lowers the risk of peri-prosthetic fractures, reduce leg length discrepancy and ensure the required implants are available. If a marker is not included on the initial X-rays, and a patient has a neck of femur fracture which requires a joint replacement, they may have to have additional X-rays performed as was the case for 12 patients in this study. This process leads to possible delays in surgery, additional radiation and increased healthcare costs

We have investigated the accuracy of the templating of digital radiographs in planning total hip replacement using two common object-based calibration methods with the ball placed laterally (method 1) or medially (method 2) and compared them with two non-object-based methods. The latter comprised the application of a fixed magnification of 121% (method 3) and calculation of magnification based on the object-film-distance (method 4). We studied the post-operative radiographs of 57 patients (19 men, 38 women, mean age 73 years (53 to 89)) using the measured diameter of the prosthetic femoral head and comparing it with the true value. Both object-based methods (1 and 2) produced large errors (mean/maximum: 2.55%/17.4% and 2.04%/6.46%, respectively). Method 3 applying a fixed magnification and method 4 (object-film-distance) produced smaller errors (mean/maximum 1.42%/5.22% and 1.57%/4.24%, respectively; p < 0.01). The latter results were clinically relevant and acceptable when planning was allowed to within one implant size. Object-based calibration (methods 1 and 2) has fundamental problems with the correct placement of the calibration ball. The accuracy of the fixed magnification (method 3) matched that of object-film-distance (method 4) and was the most reliable and efficient calibration method in digital templating

Digital radiography is becoming widespread. Accurate pre-operative templating of digital images of the hip traditionally involves positioning a calibration object at its centre. This can be difficult and cause embarrassment. We have devised a method whereby a planar disc placed on the radiographic cassette accounts for the expected magnification. Initial examination of 50 pelvic CT scans showed a mean hip centre distance of 117 mm (79 to 142) above the gluteal skin. Further calculations predicted that a disc of 37.17 mm diameter, placed on the cassette, would appear identical to a 30 mm sphere placed at the level of the centre of the hip as requested by our templating software. We assessed accuracy and reproducibility by ‘reverse calibration’ of 20 radiographs taken three months after hip replacement using simultaneous sphere and disc methods, and a further 20 with a precision disc of accurate size. Even when variations in patient size were ignored, the disc proved more accurate and reliable than the sphere. The technique is reliable, robust, cost effective and acceptable to patients and radiographers. It can easily be used in any radiography department after a few simple calculations and manufacture of appropriately-sized discs

One of the advantages of Computer Assisted Orthopaedic Surgery is to obtain functional and morphological information in real time during the procedure. 3D models can be built, without preoperative images, based on elastic 3D to 3D registration methods. The bone morphing algorithm is one of them. It allows to specifically build the 3D shape of bones using a deformable model and a set of spare points obtained on the patient. These points are obtained with a pointer tracker visible by the station which digitises the surface of the bone. However, it’s not always possible to digitise directly the bone in the context of minimal invasive surgery. In this case, the lack of information leads to an inaccurate reconstruction of bone’s surfaces. To collect such missing information we propose to rely on ultrasound (US) images despite the fact that ultrasound is not the best modality to image bones. To use this method, a segmentation step is first needed to detect automatically the bone in US images. Then, a calibration step of the US probe is carried out to obtain the 3D position of any point of the 2D ultrasonic images using 3D infra-red localizer. Several methods can be carried out to calibrate US probes, however to take into account surgical constraints such as accuracy, robustness, speed and ease of use, we decided to implement the single wall procedure. The calibration step consists in the estimation of a transformation matrix which carries out the connection between the 2D reference system of the US image and a 3D reference system in the space. To estimate correctly this matrix, a wall is scanned with different motions of the US probe. The images are then processed to automatically detect the lines representing the wall in the US images. A preliminary step allows to clean the images using a threshold and a gradient operation. Then, a method based on the Hough transform detects the lines on the images. Once all the images are processed, the calibration parameters can be estimated by using a new method which minimises the distance between the real plane and the points obtained with the US images. This optimisation step is composed of the genetic algorithms and of the Levenberg-Marquardt (LM) method. The first algorithm allows to obtain a good initialisation in a defined space for the LM algorithm. This good initialisation found thanks to the stochastic behaviour of the genetic algorithms is very important otherwise the LM algorithm could detect local minimum and the calibration parameters could be wrong. The accuracy of the calibration method is assessed by measuring the distance between the position of a known point in the space and the same point obtained with the US image and the calibration. 40 calibrations matrices are used to estimate correctly the accuracy. An average accuracy of 1.22 mm and a standard deviation (Std. Dev.) of 0.42 mm are measured. The accuracy of the system is quite high but the reproducibility is too low to use this approach in a clinical environment. The main reason of this lack of reproducibility is the thickness of the US beam. A slight modification in the design of the calibration tool will allow to increase the reproducibility. We will then have an efficient and automatic calibration procedure with the required accuracy and robustness, usable for clinical purposes

Introduction. Dislocation continues to be a common complication of total hip arthroplasty (THA). Many factors affect the prevalence of dislocation after THA, including soft tissue laxity, surgical approach, component position, patient factors, and component design [1]. Achieving proper intraoperative soft tissue tension is one of the surgical goals to reduce the risk of the dislocation. However, reports of the intraoperative soft tissue tension measurements have not been enough yet. One way to quantify the intraoperative soft tissue tension is to measure joint forces using an instrumented prosthesis. Hence, we have developed a sensor-instrumented modular femoral head of THA to measure the soft-tissue tension intraoperatively. The goal of this study was to design and calibrate the sensor. Materials and Methods. The sensor-instrumented modular femoral head that we developed was made of polycarbonate with four linear strain gauges (BTM-1C, Tokyo Sokki Kenkyujo Co., Ltd., JP). To fabricate the sensor, four penetrant holes (1.6 millimeter in diameter), parallel to the coordinate axes were produced (Fig1). The strain gauges were embedded on inside wall of these holes. Finally, the holes were filled by epoxy resin (A-2 adhesive, Tokyo Sokki Kenkyujo Co., Ltd., JP). For calibration study, the sensor was fixed in a clamping block of an angle vice to permit change of force directions. The calibration jig with the angle vice was placed on top of a low-friction x-y translation table that eliminated horizontal constrains. Known forces (F. i. ) were applied by a standard material testing machine (Instron4204, INSTRON, Norwood, MA) through a polyethylene insert (Fig. 2). Two different series of forces were applied. One is that force values were increased from zero to 600 N on the z axis. And the other force pattern is 600 N forces were applied by changing force angles. The external force vector (F. i. ) can be expressed in terms of the strain gauge outputs as follows:. F. i. = T S. i. where T is a calibration matrix and S. i. corresponds to the outputs of the strain gauges. Calibration errors were calculated according to well-established methods [2]. Results. When the loads were applied on the z axis, the output strains of ε1 showed increases with increase of the force values (Fig.3). A coefficient of determination of least square linear regression between εz and the force values was 0.86. When the cone angle was decreased from 90˚ in the x-z plane, εz decreased and εx increased concurrently (Fig.4). The average absolute error of the force was 23.9%. Discussion. This device was connected to a data logger with wires. In order to remove these wires to diminish the risk of infection, we will use wireless system (nRF24LE1, Nordic Semiconductor, Inc., Norway). Although the calibration matrix and the errors were acquired, the error value was not good enough to calculate the applied forces yet. With more calibration results and wireless system, this system will be useful to permit optimized intraoperative soft tissue tension

The limiting factor in the growth of RSA as a wide spread clinical tool is the man-hours needed to run a study. Calibration takes more than half of the processing time. The aim of this study is to develop automatic calibration method applicable to the grid and line patterns common in all RSA systems. This method uses a Harris Corner detector to find candidate positions on an image one 16th the original area (16 times quicker). Canny edge detection in regions of interest around the candidate positions on the full size image produce circular edges for marker-balls. A conic section is fitted to this edge using the Bookstein method to produce an accurate estimation of position to a local accuracy of 0.01 mm. Scanner distortion was modeled using a stabilised B-spline mesh to produce global accuracy of 0.03mm. A model based pattern recognition method can be used to label the marker-balls correctly. For sets of 4 marker balls a Homography was calculated and used to predict the positions of the other points in the grid. If supporting marker-balls are found in the predicted positions, they are counted. The four-point set, which returns the greatest number of support marker-balls, is the best estimate of a grid. Reference markers in the grid are used to localise it. The method had a ninety- percent success rate on a set of 20 clinical X-rays. In two X-rays not enough marker-balls were visible due to a poor exposure. It finds marker-balls in a 15-MB image in 50 seconds on a 180 MHz silicon graphics O2. Labelling speed depends on the number of marker-balls and is 45 seconds per group of 50. This method is widely implementable, as it requires just the 3D positions of the markers in each plate of the calibration object for input

An uncomplicated, quantitative method of determining density from X-rays would be of extreme value to clinicians. In this study we perform a thorough assessment of applying a step wedge to grey level calibration method to X-rays obtained using Computed Radiography (CR). An Aluminium step wedge of ten, 5mm-thick steps was X-rayed with a Fuji CR system together with a knee phantom (3M) at various energy and Fuji processing settings. Automatic detection of the steps by means of the Hough transform was used to assess optimum CR settings. Background variation due to the anode Heel effect was evaluated by acquiring an “empty field” X-ray at different energy settings and with copper filtering. The effects of beam hardening were considered with a custom-made phantom which was also used to assess correcting for soft tissue and bone thickness. X-rays taken at higher energy settings and with wider windowing imaged the widest number of steps (nine) and gave the best accuracy in modelling the step thickness to grey level relationship. Fitting a straight line to the log of the net grey levels gives an excellent model of the data (R2 = 0.99). X-rays of copper sheeting show that automatic histogram analysis is performed by the Fuji CR system, which can have unpredictable effects on aluminium thickness to grey level relationship. Background variation in the anode-cathode direction due to the Heel effect was corrected with a 1D exponential model (R2 = 0.99), allowing position-independent measurements to be obtained. Correcting for bone thickness, soft tissue and beam hardening further improves measurement quality. Use of step wedge calibration to provide quantitative information on plain X-rays without altering their clinical quality is possible using digital radiography. However, a thorough assessment of the entire X-ray process is necessary to achieve accurate and comparable information

Intraoperative planning of knee replacement components, targeting a desired functional outcome, requires a calibrated patient-specific model of the patient's soft-tissue anatomy and mechanics. Previously, a surgical technique was demonstrated for measuring knee joint kinematics and kinetics consistent with modern navigation systems in conjunction with the development of a patient-customizable knee model. A data efficient approach for the model calibration task was achieved utilizing the sensitivity of the model to simulated clinical hand manipulations of the knee joint requiring 85% less computations. For this numerical investigation a simplified knee joint model, based on the OpenKnee repository, consisting of bone (rigid), cruciate ligaments (single-bundle, nonlinear spring), collateral ligaments (multiple nonlinear springs), articular cartilage (rigid, pressure-over-closure relationship), and combined capsule/meniscus (linear springs) was created using a custom Matlab (MathWorks)-Abaqus (Dassault Systèmes) implicit finite element modeling framework (Figure 1). A sensitivity analysis was performed by applying constant loading along the anterior-posterior, medial-lateral, varus-valgus, and internal-external directions (30 N for forces and 3 Nm for moments) while perturbing each customizable parameter positively and negatively by 1 mm at 0, 25, 50, 75 and 100 degrees of flexion. A constant load of 150 N was maintained in compression. The change in static endpoint position was measured relative to the respective position without perturbation. Sensitivity results were then arranged by load direction and principal component analysis was subsequently performed (Table 1). First a single optimization task was simulated including all model parameters and all loading sequences with the goal of minimizing the kinematic differences between the reference model and a perturbed model (Figure 2). Second, a piecewise optimization task was designed using only the sensitive parameters for a spanning set of loads for the same perturbed model. Parameters 3 and 4 were tuned using internal and external endpoints. Then parameters 1 and 5 were tuned using the anterior endpoints. Similarly, parameters 2 and 7 were tuned using the posterior endpoints. Finally, parameter 8 was tuned using the varus endpoints. All loadings were observed to be insensitive to parameter 6 (ACL-Y). The number of model evaluations required were 2520 and 390 for the single and piecewise optimizations, respectively. The single simulation task recovered all parameters within 0.57 mm on average compared to 0.64 mm on average for the piecewise task. Kinematic errors due to the calibration technique were within 0.001 mm and 0.18 deg compared to 0.001 mm and 0.04 deg. Computational cost for the optimization task required to calibrate a patient-specific knee model was reduced while maintaining clinically relevant accuracy. This model reduction approach will further enable the rapid adoption of the technology for intraoperative planning of knee replacement components based on targeted functional outcomes

This study aims to create a novel computational workflow for frontal plane laxity evaluation which combines a rigid body knee joint model with a non-linear implicit finite-element model wherein collateral ligaments are anisotropically modelled using subject-specific, experimentally calibrated Holzpfel-Gasser-Ogden (HGO) models.

The framework was developed based on CT and MRI data of three cadaveric post-TKA knees. Bones were segmented from CT-scans and modelled as rigid bodies in a multibody dynamics simulation software (MSC Adams/view, MSC Software, USA). Medial collateral and lateral collateral ligaments were segmented based on MRI-scans and are modelled as finite elements using the HGO model in Abaqus (Simulia, USA). All specimens were submitted varus/valgus loading (0-10Nm) while being rigidly fixed on a testing bench to prevent knee flexion. In subsequent computer simulations of the experimental testing, rigid bodies kinematics and the associated soft-tissue force response were computed at each time step. Ligament properties were optimised using a gradient descent approach by minimising the error between the experimental and simulation-based kinematic response to the applied varus/valgus loads. For comparison, a second model was defined wherein collateral ligaments were modelled as nonlinear no-compression spring elements using the Blankevoort formulation.

Models with subject-specific, experimentally calibrated HGO representations of the collateral ligaments demonstrated smaller root mean square errors in terms of kinematics (0.7900° +/− 0.4081°) than models integrating a Blankevoort representation (1.4704° +/− 0.8007°).

A novel computational workflow integrating subject-specific, experimentally calibrated HGO predicted post-TKA frontal-plane knee joint laxity with clinically applicable accuracy. Generally, errors in terms of tibial rotation were higher and might be further reduced by increasing the interaction nodes between the rigid body model and the finite element software. Future work should investigate the accuracy of resulting models for simulating unseen activities of daily living.

Aims

To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis.

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

Aims. The Uppföljningsprogram för cerebral pares (CPUP) Hip Score distinguishes between children with cerebral palsy (CP) at different levels of risk for displacement of the hip. The score was constructed using data from Swedish children with CP, but has not been confirmed in any other population. The aim of this study was to determine the calibration and discriminatory accuracy of this score in children with CP in Scotland. Methods. This was a total population-based study of children registered with the Cerebral Palsy Integrated Pathway Scotland. Displacement of the hip was defined as a migration percentage (MP) of > 40%. Inclusion criteria were children in Gross Motor Function Classification System (GMFCS) levels III to V. The calibration slope was estimated and Kaplan-Meier curves produced for five strata of CPUP scores to compare the observed with the predicted risk of displacement of the hip at five years. For discriminatory accuracy, the time-dependent area under the receiver operating characteristic curve (AUC) was estimated. In order to analyze differences in the performance of the score between cohorts, score weights, and subsequently the AUC, were re-estimated using the variables of the original score: the child’s age at the first examination, GMFCS level, head shaft angle, and MP of the worst hip in a logistic regression with imputation of outcomes for those with incomplete follow-up. Results. The discriminatory accuracy of the score in the new population of 367 children was high (AUC 0.78 (95% confidence interval (CI) 0.71 to 0.86)). The calibration of the score was insufficient (slope 0.48 (95% CI 0.31 to 0.65)), and the absolute risks of displacement of the hip in this population were overestimated. The AUC increased with re-estimated weights (0.85 (95% CI 0.79 to 0.91)). Conclusion. The CPUP Hip Score had a high ability to discriminate between children at different levels of risk for displacement of the hip. The score overestimated the absolute risks of displacement in this population, which may have resulted from differences in the way children were initially registered in the two programmes. The results are promising, but the score weights may need re-estimation before its clinical application in Scotland. Cite this article: Bone Joint J 2022;104-B(5):640–644

Aims. Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries. Methods. This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation. Results. Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes. Conclusion. The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963–971

Aims. The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow. Methods. We developed the Shanghai Prediction Model for Elbow Stiffness Surgical Outcome (SPESSO) based on a dataset of 551 patients who underwent open arthrolysis of the elbow in four institutions. Demographic and clinical characteristics were collected from medical records. The least absolute shrinkage and selection operator regression model was used to optimize the selection of relevant features. Multivariable logistic regression analysis was used to build the SPESSO. Its prediction performance was evaluated using the concordance index (C-index) and a calibration graph. Internal validation was conducted using bootstrapping validation. Results. BMI, the duration of stiffness, the preoperative ROM, the preoperative intensity of pain, and grade of post-traumatic osteoarthritis of the elbow were identified as predictors of outcome and incorporated to construct the nomogram. SPESSO displayed good discrimination with a C-index of 0.73 (95% confidence interval 0.64 to 0.81). A high C-index value of 0.70 could still be reached in the interval validation. The calibration graph showed good agreement between the nomogram prediction and the outcome. Conclusion. The newly developed SPESSO is a valid and convenient model which can be used to predict the outcome of open arthrolysis of the elbow. It could assist clinicians in counselling patients regarding the choice and expectations of treatment. Cite this article: Bone Joint J 2022;104-B(4):486–494

Several different algorithms attempt to estimate life expectancy for patients with metastatic spine disease. The Skeletal Oncology Research Group (SORG) has recently developed a nomogram to estimate survival of patients with metastatic spine disease. Whilst the use of the SORG nomogram has been validated in the international context, there has been no study to date that validates the use of the SORG nomogram in New Zealand. This study aimed to validate the use of the SORG nomogram in Aotearoa New Zealand. We collected data on 100 patients who presented to Waikato Hospital with a diagnosis of spinal metastatic disease. The SORG nomogram gave survival probabilities for each patient at each time point. Receiver Operating Characteristic (ROC) Area Under Curve (AUC) analysis was performed to assess the predictive accuracy of the SORG score. A calibration curve was also performed, and Brier scores calculated. A multivariate Cox regression analysis was performed. The SORG score was correlated with 30 day (AUC = 0.72) and 90-day mortality (AUC = 0.71). The correlation between the SORG score and 90-day mortality was weaker (AUC = 0.69). Using this method, the nomogram was correct for 79 (79%) patients at 30-days, 59 patients (59%) at 90-days, and 42 patients (42%) at 365-days. Calibration curves demonstrated poor forecasting of the SORG nomogram at 30 (Brier score = 0.65) and 365 days (Brier score = 0.33). The calibration curve demonstrated borderline forecasting of the SORG nomogram at 90 days (Brier score = 0.28). Several components of the SORG nomogram were not found to be correlated with mortality. In this New Zealand cohort the SORG nomogram demonstrated only acceptable discrimination at best in predicting life 30-, 90- or 356-day mortality in patients with metastatic spinal disease

Approximately 20% of patients feel unsatisfied 12 months after primary total knee arthroplasty (TKA). Current predictive tools for TKA focus on the clinician as the intended user rather than the patient. The aim of this study is to develop a tool that can be used by patients without clinician assistance, to predict health-related quality of life (HRQoL) outcomes 12 months after total knee arthroplasty (TKA). All patients with primary TKAs for osteoarthritis between 2012 and 2019 at a tertiary institutional registry were analysed. The predictive outcome was improvement in Veterans-RAND 12 utility score at 12 months after surgery. Potential predictors included patient demographics, co-morbidities, and patient reported outcome scores at baseline. Logistic regression and three machine learning algorithms were used. Models were evaluated using both discrimination and calibration metrics. Predictive outcomes were categorised into deciles from 1 being the least likely to improve to 10 being the most likely to improve. 3703 eligible patients were included in the analysis. The logistic regression model performed the best in out-of-sample evaluation for both discrimination (AUC = 0.712) and calibration (gradient = 1.176, intercept = -0.116, Brier score = 0.201) metrics. Machine learning algorithms were not superior to logistic regression in any performance metric. Patients in the lowest decile (1) had a 29% probability for improvement and patients in the highest decile (10) had an 86% probability for improvement. Logistic regression outperformed machine learning algorithms in this study. The final model performed well enough with calibration metrics to accurately predict improvement after TKA using deciles. An ongoing randomised controlled trial (ACTRN12622000072718) is evaluating the effect of this tool on patient willingness for surgery. Full results of this trial are expected to be available by April 2023. A free-to-use online version of the tool is available at . smartchoice.org.au.

Aims. To develop and externally validate a parsimonious statistical prediction model of 90-day mortality after elective total hip arthroplasty (THA), and to provide a web calculator for clinical usage. Methods. We included 53,099 patients with cemented THA due to osteoarthritis from the Swedish Hip Arthroplasty Registry for model derivation and internal validation, as well as 125,428 patients from England and Wales recorded in the National Joint Register for England, Wales, Northern Ireland, the Isle of Man, and the States of Guernsey (NJR) for external model validation. A model was developed using a bootstrap ranking procedure with a least absolute shrinkage and selection operator (LASSO) logistic regression model combined with piecewise linear regression. Discriminative ability was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration belt plots were used to assess model calibration. Results. A main effects model combining age, sex, American Society for Anesthesiologists (ASA) class, the presence of cancer, diseases of the central nervous system, kidney disease, and diagnosed obesity had good discrimination, both internally (AUC = 0.78, 95% confidence interval (CI) 0.75 to 0.81) and externally (AUC = 0.75, 95% CI 0.73 to 0.76). This model was superior to traditional models based on the Charlson (AUC = 0.66, 95% CI 0.62 to 0.70) and Elixhauser (AUC = 0.64, 95% CI 0.59 to 0.68) comorbidity indices. The model was well calibrated for predicted probabilities up to 5%. Conclusion. We developed a parsimonious model that may facilitate individualized risk assessment prior to one of the most common surgical interventions. We have published a web calculator to aid clinical decision-making. Cite this article: Bone Joint J 2021;103-B(3):469–478

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