Abstract

Aims

Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds.

Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS.

Cite this article: Bone Joint J 2022;104-B(9):1011–1016.

Patients with cancer and bone metastases can have an increased risk of fracturing their femur. Treatment is based on the impending fracture risk: patients with a high fracture risk are considered for prophylactic surgery, whereas low fracture risk patients are treated conservatively with radiotherapy to decrease pain. Current clinical guidelines suggest to determine fracture risk based on axial cortical involvement of the lesion on conventional radiographs, but that appears to be difficult. Therefore, we developed a patient-specific finite element (FE) computer model that has shown to be able to predict fracture risk in an experimental setting and in patients. The goal of this study was to determine whether patient-specific finite element (FE) computer models are better at predicting fracture risk for femoral bone metastases compared to clinical assessments based on axial cortical involvement on conventional radiographs, as described in current clinical guidelines.

45 patients (50 affected femurs) affected with predominantly lytic bone metastases who were treated with palliative radiotherapy for pain were included. CT scans were made and patients were followed for six months to determine whether or not they fractured their femur. Non-linear isotropic FE models were created with the patient-specific geometry and bone density obtained from the CT scans. Subsequently, an axial load was simulated on the models mimicking stance. Failure loads normalized for bodyweight (BW) were calculated for each femur. High and low fracture risks were determined using a failure load of 7.5 × BW as a threshold. Experienced assessors measured axial cortical involvement on conventional radiographs. Following clinical guidelines, patients with lesions larger than 30 mm were identified as having a high fracture risk. FE predictions were compared to clinical assessments by means of diagnostic accuracy values (sensitivity, specificity and positive (PPV) and negative predictive values (NPV)).

Seven femurs (14%) fractured during follow-up. Median time to fracture was 8 weeks. FE models were better at predicting fracture risk in comparison to clinical assessments based on axial cortical involvement (sensitivity 100% vs. 86%, specificity 74% vs. 42%, PPV 39% vs. 19%, and NPV 100% vs. 95%, for the FE computer model vs. axial cortical involvement, respectively). We concluded that patient-specific FE computer models improve fracture risk predictions of femoral bone metastases in advanced cancer patients compared to clinical assessments based on axial cortical involvement, which is currently used in clinical guidelines. Therefore, we are initiating a pilot for clinical implementation of the FE model.

Abstract

Aims

The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors.

Aims

To devise a method to quantify and optimize tightness when inserting cortical screws, based on bone characterization and screw geometry.

Introduction. Gait laboratory measurement of whole-body kinematics and ground reaction forces during a wide range of activities is frequently performed in joint replacement patient diagnosis, monitoring, and rehabilitation programs. These data are commonly processed in musculoskeletal modeling platforms such as OpenSim and Anybody to estimate muscle and joint reaction forces during activity. However, the processing required to obtain musculoskeletal estimates can be time consuming, requires significant expertise, and thus seriously limits the patient populations studied. Accordingly, the purpose of this study was to evaluate the potential of deep learning methods for estimating muscle and joint reaction forces over time given kinematic data, height, weight, and ground reaction forces for total knee replacement (TKR) patients performing activities of daily living (ADLs). Methods. 70 TKR patients were fitted with 32 reflective markers used to define anatomical landmarks for 3D motion capture. Patients were instructed to perform a range of tasks including gait, step-down and sit-to-stand. Gait was performed at a self-selected pace, step down from an 8” step height, and sit-to-stand using a chair height of 17”. Tasks were performed over a force platform while force data was collected at 2000 Hz and a 14 camera motion capture system collected at 100 Hz. The resulting data was processed in OpenSim to estimate joint reaction and muscle forces in the hip and knee using static optimization. The full set of data consisted of 135 instances from 70 patients with 63 sit-to-stands, 15 right-sided step downs, 14 left-sided step downs, and 43 gait sequences. Two classes of neural networks (NNs), a recurrent neural network (RNN) and temporal convolutional neural network (TCN), were trained to predict activity classification from joint angle, ground reaction force, and anthropometrics. The NNs were trained to predict muscle and joint reaction forces over time from the same input metrics. The 135 instances were split into 100 instances for training, 15 for validation, and 20 for testing. Results. The RNN and TCN yielded classification accuracies of 90% and 100% on the test set. Correlation coefficients between ground truth and predictions from the test set ranged from 0.81–0.95 for the RNN, depending on the activity. Predictions from both NNs were qualitatively assessed. Both NNs were able to effectively learn relationships between the input and output variables. Discussion. The objective of the study was to develop and evaluate deep learning methods for predicting patient mechanics from standard gait lab data. The resulting models classified activities with excellent performance, and showed promise for predicting exact values for loading metrics for a range of different activities. These results indicate potential for real-time prediction of musculoskeletal metrics with application in patient diagnostics and rehabilitation. For any figures or tables, please contact authors directly

Aims

Open fractures of the tibia are a heterogeneous group of injuries that can present a number of challenges to the treating surgeon. Consequently, few surgeons can reliably advise patients and relatives about the expected outcomes. The aim of this study was to determine whether these outcomes are predictable by using the Ganga Hospital Score (GHS). This has been shown to be a useful method of scoring open injuries to inform wound management and decide between limb salvage and amputation.

Objectives

X-linked hypophosphataemic rickets (XLHR) is a disease of impaired bone mineralization characterized by hypophosphataemia caused by renal phosphate wasting. The main clinical manifestations of the disorder are O-shaped legs, X-shaped legs, delayed growth, and bone pain. XLHR is the most common inheritable form of rickets, with an incidence of 1/20 000 in humans. It accounts for approximately 80% of familial cases of hypophosphataemia and serves as the prototype of defective tubular phosphate (PO4³⁺) transport, due to extra renal defects resulting in unregulated FGF23 activity. XLHR is caused by loss-of-function mutations in the PHEX gene. The aim of this research was to identify the genetic defect responsible for familial hypophosphataemic rickets in a four-generation Chinese Han pedigree and to analyze the function of this mutation.

Objectives. Opening wedge high tibial osteotomy (HTO) is an established surgical procedure for the treatment of early-stage knee arthritis. Other than infection, the majority of complications are related to mechanical factors – in particular, stimulation of healing at the osteotomy site. This study used finite element (FE) analysis to investigate the effect of plate design and bridging span on interfragmentary movement (IFM) and the influence of fracture healing on plate stress and potential failure. Materials and Methods. A 10° opening wedge HTO was created in a composite tibia. Imaging and strain gauge data were used to create and validate FE models. Models of an intact tibia and a tibia implanted with a custom HTO plate using two different bridging spans were validated against experimental data. Physiological muscle forces and different stages of osteotomy gap healing simulating up to six weeks postoperatively were then incorporated. Predictions of plate stress and IFM for the custom plate were compared against predictions for an industry standard plate (TomoFix). Results. For both plate types, long spans increased IFM but did not substantially alter peak plate stress. The custom plate increased axial and shear IFM values by up to 24% and 47%, respectively, compared with the TomoFix. In all cases, a callus stiffness of 528 MPa was required to reduce plate stress below the fatigue strength of titanium alloy. Conclusion. We demonstrate that larger bridging spans in opening wedge HTO increase IFM without substantially increasing plate stress. The results indicate, however, that callus healing is required to prevent fatigue failure. Cite this article: A. R. MacLeod, G. Serrancoli, B. J. Fregly, A. D. Toms, H. S. Gill. The effect of plate design, bridging span, and fracture healing on the performance of high tibial osteotomy plates: An experimental and finite element study. Bone Joint Res 2018;7:639–649. DOI: 10.1302/2046-3758.712.BJR-2018-0035.R1

The screw fastening torque applied during bone fracture fixation has a decisive influence on subsequent bone healing. Insufficient screw tightness can result in device/construct instability; conversely, excessive torques risk damaging the bone causing premature fixation failure. This effect is even more prominent in osteoporotic bone, a condition associated annually with almost 9 million fractures worldwide. During fracture fixation, screw tightening torque is applied using subjective feel. This approach may not be optimal for patient”s recovery, increasing risk of fixation failure, particularly in osteoporotic bone, and potentially require revision surgical interventions.

Besides bone density, various factors influence the performance of screw fixation. These factors include bone geometry, cortical thickness and time-dependant relaxation behaviour of the bone. If the influence of screw fastening torque on the bone and relationships between these factors was better understood, the surgical technique could be optimised to reduce the risk of complications.

Within this study, we developed an axisymmetric finite element (FE) model of bone screw tightening incorporating viscoelastic behaviour of the cortical bone such as creep and stress relaxation. The model anticipated time-dependent behaviour of the bone for different bone thickness and density after a typical bone fixation screw had been inserted. The idealised model has been developed based on CT scans of bones with varying densities and inserted screws. The model was validated through a series of experiments involving bovine tibiae (4–5 months) to evaluate the evolution of surface strains with time (Ncorr v1.2). Stress distribution was assessed in photoelastic experiments using acrylic analogues. Relaxation tests have been performed in aqueous environment for up to 48 hours to ensure the relaxation would be complete. The creep behaviour (maximum principal strain) was compared against computational predictions. Our early simulations predicted relaxation strains on the surface of the bone to be 1.1% within 24 hours comparing favourably to 1.3% measured experimentally. Stress distribution patterns were in agreement with photoelastic results.

Using experimentally derived viscoelastic properties, the model has the potential to predict creep and stress relaxation patterns after screw insertion with different fastening torques for bones with varying density and geometry. We aim to develop this into a planning tool providing guidance to surgeons for optimal tightening when using screw fixation, particularly in reduced quality bone.

Objectives

Secondary fracture healing is strongly influenced by the stiffness of the bone-fixator system. Biomechanical tests are extensively used to investigate stiffness and strength of fixation devices. The stiffness values reported in the literature for locked plating, however, vary by three orders of magnitude. The aim of this study was to examine the influence that the method of restraint and load application has on the stiffness produced, the strain distribution within the bone, and the stresses in the implant for locking plate constructs.

Total shoulder arthroplasty is a well-tested procedure that offers pain relief and restores the joint function. However, failure rate is still high, and glenoid loosening is pointed as the main reason in orthopedic registers. In order to understand the principles of failure, the principal strain distributions after implantation with Comprehensive® Total Shoulder System of Biomet® were experimental and numerically studied to predict bone behavior.

Fourth generation composite left humerus and scapula from Sawbones® were used. These were implanted with Comprehensive® Total Shoulder System (Biomet®) with a modular Hybrid® glenoid base and Regenerex® glenoid and placed in situ by an experienced surgeon. The structures were placed in order to simulate 90º abduction, including principal muscular actions. Muscle forces used were as follows: Deltoideus 300N, Infraspinatus 120N, Supraspinatus 90N, Subscapularis 225N.

All bone structures were modeled considering cortical and the trabecular bone of the scapula. The components of prosthesis were placed in the same positions than those in the in vitro models. Geometries were meshed with tetrahedral linear elements, with material properties as follows: Elastic modulus of cortical bone equal to 16 GPa, elastic modulus of trabecular bone equal to 0.155 GPa, polyethylene equal to 1GPa and titanium equal to 110 GPa. The assumed Poisson's ratio was 0.3 in all except for polyethylene where we assumed a value of 0.4. The prosthesis was considered as glued to the adjacent bone. The finite element model was composed of 336 024 elements.

At the glenoid cavity, the major influence of the strain distributions was observed at the posterior-superior region, in both cortical and trabecular bone structures. The system presents critical region around holes of fixation in glenoid component. At the trabecular bone, the maximum principal strains at the posterior-superior region ranged from 2250 µε to 3000 µε. While at the cortical bone, the maximum principal strains were 300 µε to 400 µε.

The results observed evidence some critical regions of concern and the effect of implant in the bone strains mainly at the posterior-superior region of the glenoid cavity is pronounced. This indicates that this region is more affected by the implant if bone remodeling is a concern and it is due to the strain-shielding effect, which has been connected with loosening of the glenoid component.

Polyethylene wear of joint replacements can cause severe clinical complications, including; osteolysis, implant loosening, inflammation and pain. Wear simulator testing is often used to assess new designs, but it is expensive and time consuming. It is possible to predict the volume of polyethylene implant wear from finite element models using a modification of Archard's classic wear law [1–2]. Typically, linear elastic isotropic, or elasto-plastic material models are used to represent the polyethylene. The purpose of this study was to investigate whether use of a viscoelastic material model would significantly alter the predicted volumetric wear of a mobile-bearing unicompartmental knee replacement.

Tensile creep-recovery experiments were performed to characterise the creep and relaxation behaviour of the polyethylene (moulded GUR 4150 samples machined to 180×20×1 mm). Samples were loaded to 3 MPa stress in 4 minutes, and then held for 6 hours, the tensile stress was removed and samples were left to relax for 6 hours. The mechanical test data was used fit to a validated three–dimensional fractional Maxwell viscoelastic constitutive material model [3]. An explicit finite element model of a mobile–bearing unicompartmental knee replacement was created, which has been described previously [4]. The medial knee replacement was loaded to 1200 N over a period of 0.2 s. The bearing was meshed using quadratic tetrahedral elements (1.5 mm seeding size based on results of a mesh convergence study), and the femoral component was represented as an analytical rigid body. Wear predictions were made from the contact stress and sliding distance using Archard's law, as has been described in the literature [1–2]. A wear factor of 5.24×10⁻¹¹ was used based upon the work by Netter et al. [2]. All models were created and solved using ABAQUS finite element software (version 6.14, Simulia, Dassault Systemes).

The fractional viscoelastic material model predicted almost twice as much wear (0.119 mm³/million cycles) compared to the elasto-plastic model (0.069 mm³/million cycles). The higher wear prediction was due to both an increased sliding distance and higher contact pressures in the viscoelastic model.

These preliminary findings indicate the simplified elasto-plastic polyethylene material representation can underestimate wear predictions from numerical simulations. Polyethylene is known to be a viscoelastic material which undergoes creep clinically, and it is not surprising that it is necessary to represent that viscoelastic behaviour to accurately predict implant wear. However, it does increase the complexity and run time of such computational studies, which may be prohibitive.

The Stanford Upper Extremity Model (SUEM) (Holzbauer, Murray, Delp 2005, Ann Biomed Eng) includes the major muscles of the upper limb and has recently been described in scientific literature for various biomechanical purposes including modeling the muscle behavior after shoulder arthroplasty (Hoenecke, Flores-Hernandez, D'Lima 2014, J Shoulder Elbow Surg; Walker, Struk, Banks 2013, ISTA Proceedings). The initial publication of the SUEM compared the muscle moment arm predictions of the SUEM against various moment arm studies and all with the scapula fixed. A more recent study (Ackland, Pak, and Pandy 2008, J Anat) is now available that can be used to compare SUEM moment arm predictions to cadaver data for similar muscle sub-regions, during abduction and flexion motions, and with simulated scapular motion.

SUEM muscle moment arm component vectors were calculated using the OpenSim Analyze Tool for an idealized abduction and an idealized flexion motion from 10° to 90° that corresponded to the motions described in Ackland for the cadaver arms. The normalized, averaged muscle moment arm data for the cadavers was manually digitized from the published figures and then resampled into uniform angles matching the SUEM data. Standard deviations of the muscle moment arms from the cadaver study were calculated from source data provided by the study authors. Python code was then used to calculate the differences, percent differences, and root-mean-square (RMS) values between the data sets.

Of the 14 muscle groups in the SUEM, the smallest difference in predicted and measured moment arm was for the supraspinatus during the abduction task, with an RMS of the percent difference of 11.4%. In contrast, the middle latissimus dorsi had an RMS percent difference over 400% during the flexion task. The table presents the RMS difference and the RMS of the percent difference for the muscles with the largest abduction and adduction moment arms (during abduction) and the largest flexion and extension moment arms (during flexion). The moment arm data for the SUEM model and the cadaver data (with 1 standard deviation band) during the motion of the same muscles are provided in Figure 1 for the Abduction motion task and in Figure 2 for the Flexion motion task.

It is challenging to simulate the three dimensional, time variant geometries of shoulder muscles while maintaining model fidelity and optimizing computational cost. Dividing muscles in to sub regions and using wrapping line segment approximations appears a reasonable strategy though more work could improve model accuracy especially during complex three dimensional motions.

Predictions of lower limb growth are based upon historical data, collected from patients who had coexistent poliomyelitis. By utilising standardised longitudinal prospective European data, our objective was to generate superior estimates for the age and rate at which lower limb skeletal maturity is reached; thus improving the timing of epiphysiodesis, for the management of leg length discrepancy. The Avon Longitudinal Study of Parents and Children of the 90s (ALSPAC) is a longitudinal cohort study of children recruited antenatally 2. Using a previously validated Multiplier Method, a sequence of leg length multipliers were calculated for each child. 15,458 individuals were recruited to the ALSPAC study; and of those whose growth was measured, 52% were boys and 48% girls, each with an average of eight recording episodes. 25,828 leg length multiplier (LLM) values were calculated with final recordings taken at a mean age of 15.5 years. From this data, the age at which girls reach skeletal maturity (LLM=1) is 11 months later than previously calculated and for boys nearly 9 months earlier. With nearly 4000 more children recruited in this cohort than preceding studies, this study brings increased power to future leg length calculations

Summary Statement. A coupled finite element - analytical model is presented to predict and to elucidate a clinical healing scenario where bone regenerates in a critical-sized femoral defect, bounded by periosteum or a periosteum substitute implant and stabilised via an intramedullary nail. Introduction. Bone regeneration and maintenance processes are intrinsically linked to mechanical environment. However, the cellular and subcellular mechanisms of mechanically-modulated bone (re-) generation are not fully understood. Recent studies with periosteum osteoprogenitor cells exhibit their mechanosensitivity in vitro and in situ. In addtion, while a variety of growth factors are implicated in bone healing processes, bone morphogenetic protein-2 (BMP-2) is recognised to be involved in all stages of bone regeneration. Furthermore, periosteal injuries heal predominantly via endochondral ossification mechanisms. With this background in mind, the current study aims to understand the role of mechanical environment on BMP-2 production and periosteally-mediated bone regeneration. The one-stage bone transport model [1] provides a clinically relevant experimental platform on which to model the mechanobiological process of periosteum-mediated bone regeneration in a critical-sized defect. Here we develop a model framework to study the cellular-, extracellular- and mechanically-modulated process of defect infilling, governed by the mechanically-modulated production of BMP-2 by osteoprogenitor cells located in the periosteum. Methods. Material properties of the healing callus and periosteum contribute to the strain stimulus sensed by osteoprogenitor cells therein. Using a mechanical finite element model, periosteal surface strains are first predicted as a function of callus properties. Strains are then input to a mechanistic mathematical model, where mechanical regulation of BMP-2 production mediates rates of cellular proliferation, differentiation and extracellular matrix (ECM) production, to predict healing outcomes. A parametric approach enables the spatial and temporal prediction of tissue regeneration via endochondral ossification. Predictions are compared with experimental, micro-computed tomographic and histologic, measures of cartilage and mineralised bone tissue regenerates. Model Predictions in Light of Experimental Case Studies: A validated baseline model predicts defect healing via cellular egression, extracellular matrix production and endochondral ossification, using parameters optimised to mimic experimental outcome measures at initial and final stages of healing. To elucidate which predictive model paramenters result in the intrinsic differences in experimental outcomes between defects bounded by either periosteum in situ or a periosteum substitute implant, model parameters are then varied by orders of magnitude to determine which factors exert dominant influence on achievement of experimentally relevant ECM area outcomes. Considering the complete set of parameters relevant to healing, the rate of osteoprogenitor to osteoblast differentiation, as well as rates of chondrocyte and osteoblast proliferation must be reduced and ECM production by chondrocytes must be increased from baseline, to achieve healing outcomes analogous to those observed in experiments. Discussion/Conclusion. The novel model framework presented here integrates a mechanistic feedback system, based on the mechanosensitivity of periosteal osteoprogenitor cells, which allows for modeling and prediction of tissue regeneration on multiple length and time scales

Introduction

Many finite element (FE) studies have been performed in the past to assess the biomechanical performance of TKA and THA components. The boundary conditions have often been simplified to a few peak loads. With the availability of personalized musculoskeletal (MS) models we becomes possible to estimate dynamic muscle and prosthetic forces in a patient specific manner. By combining this knowledge with FE models, truly patient specific failure analyses can be performed.

In this study we applied this combined technique to the femoral part of a cementless THR and calculated the cyclic micro-motions of the stem relative to the bone in order to assess the potential for bone ingrowth.

Previously, we showed that case-specific non-linear finite element (FE) models are better at predicting the load to failure of metastatic femora than experienced clinicians. In this study we improved our FE modelling and increased the number of femora and characteristics of the lesions. We retested the robustness of the FE predictions and assessed why clinicians have difficulty in estimating the load to failure of metastatic femora. A total of 20 femora with and without artificial metastases were mechanically loaded until failure. These experiments were simulated using case-specific FE models. Six clinicians ranked the femora on load to failure and reported their ranking strategies. The experimental load to failure for intact and metastatic femora was well predicted by the FE models (R² = 0.90 and R² = 0.93, respectively). Ranking metastatic femora on load to failure was well performed by the FE models (τ = 0.87), but not by the clinicians (0.11 < τ < 0.42). Both the FE models and the clinicians allowed for the characteristics of the lesions, but only the FE models incorporated the initial bone strength, which is essential for accurately predicting the risk of fracture. Accurate prediction of the risk of fracture should be made possible for clinicians by further developing FE models.