Aim. This study aimed to externally validate promising preoperative PJI prediction models in a recent, multinational European cohort. Method. Three preoperative PJI prediction models (by Tan et al., Del Toro et al., and Bülow et al.) which previously demonstrated high levels of accuracy were selected for validation. A multicenter retrospective observational analysis was performed of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) between January 2020 and December 2021 and treated at centers in the Netherlands, Portugal, and Spain. Patient characteristics were compared between our cohort and those used to develop the prediction models. Model performance was assessed through discrimination and calibration. Results. A total of 2684 patients were included of whom 60 developed a PJI (2.2%). Our patient cohort differed from the models’ original cohorts in terms of demographic variables, procedural variables, and the prevalence of comorbidities. The c-statistics for the Tan, Del Toro, and Bülow models were 0.72, 0.69, and 0.72 respectively. Calibration was reasonable, but precise percentage estimates for PJI risk were most accurate for predicted risks up to 3-4%; the Tan model overestimated risks above 4%, while the Del Toro model underestimated risks above 3%. Conclusions. In this multinational cohort study, the Tan, Del Toro, and Bülow PJI prediction models were found to be externally valid for classifying high risk patients for developing a PJI. These models hold promise for clinical application to enhance preoperative patient counseling and targeted prevention strategies. Keywords. Periprosthetic Joint Infection (PJI), High Risk Groups, Prediction Models, Validation, Infection Prevention

Introduction. Achieving an appropriate primary stability after implantation is a prerequisite for the long-term viability of a dental implant. Virtual testing of the bone-implant construct can be performed with finite element (FE) simulation to predict primary stability prior to implantation. In order to be translated to clinical practice, such FE modeling must be based on clinically available imaging methods. The aim of this study was to validate an FE model of dental implant primary stability using cone beam computed tomography (CBCT) with ex vivo mechanical testing. Method. Three cadaveric mandibles (male donors, 87-97 years old) were scanned by CBCT. Twenty-three bone samples were extracted from the bones and conventional dental implants (Ø4.0mm, 9.5mm length) were inserted in each. The implanted specimens were tested under quasi-static bending-compression load (cf. ISO 14801). Sample-specific homogenized FE (hFE) models were created from the CBCT images and meshed with hexahedral elements. A non-linear constitutive model with element-wise density-based material properties was used to simulate bone and the implant was considered rigid. The experimental loading conditions were replicated in the FE model and the ultimate force was evaluated. Result. The experimental ultimate force ranged between 67 N and 789 N. The simulated ultimate force correlated better with the experimental ultimate force (R. 2. =0.71) than the peri-implant bone density (R. 2. =0.30). Conclusion. The developed hFE model was demonstrated to provide stronger prediction of primary stability than peri-implant bone density. Therefore, hFE Simulations based on this clinically available low-radiation imaging modality, is a promising technology that could be used in future as a surgery planning tool to assist the clinician in evaluating the load-bearing capacity of an implantation site. Acknowledgements. Funding: EU's Horizon 2020 grant No: 953128 (I-SMarD). Dental implants: THOMMEN Medical AG

Introduction

Pedicle screw loosening in posterior instrumentation of thoracolumbar spine occurs up to 60% in osteoporotic patients. These complications may be alleviated using more flexible implant materials and novel designs that could be optimized with reliable computational modeling. This study aimed to develop and validate non-linear homogenized finite element (hFE) simulations to predict pedicle screw toggling.

Introduction. A recent study to identify clinically meaningful benchmarks for gait improvement after total hip replacement (THA) has shown that the minimum clinically important improvement (MCII) in gait speed after THA is 0.32 m/sec. Currently, it remains to be investigated what preoperative factors link to suboptimal recovery of gait function after THA. This study aimed to identify preoperative lower-limb muscle predictors for gait speed improvement after THA for hip osteoarthritis. Method. This study enrolled 58 patients who underwent unilateral primary THA. Gait speed improvement was evaluated as the subtraction of preoperative speed from postoperative speed at 6 months after THA. Preoperative muscle composition of the glutei medius and minimus (Gmed+min) and the gluteus maximus (Gmax) was evaluated on a single axial computed tomography slice at the bottom end of the sacroiliac joint. Cross-sectional area ratio of individual composition to the total muscle was calculated. Result. The females (n=45) showed smaller total cross-sectional areas of the gluteal muscles than the males (n=13). Gmax in the females showed lower lean muscle mass area (LMM) and higher ratios of the intramuscular fat area and the intramuscular adipose tissue area to the total muscle area (TM) than that in the males. Regression analysis revealed that LMM/TM of Gmed+min may correlate negatively with postoperative improvement in gait speed. Receiver operating characteristic curve analysis for prediction of MCII in gait speed at ≥ 0.32 m/sec resulted in the highest area under the curve for Gmax TM with negative correlation. The explanatory variables of hip abductor muscle composition predicted gait speed improvement after THA more precisely in the females compared with the total group of both sexes. Conclusion. Preoperative Gmax TM could predict gait speed MCII after THA. Preoperative muscle composition should be evaluated separately based on sexes for achievement of clinically important improvement in gait speed after THA

Aims

Though most humeral shaft fractures heal nonoperatively, up to one-third may lead to nonunion with inferior outcomes. The Radiographic Union Score for HUmeral Fractures (RUSHU) was created to identify high-risk patients for nonunion. Our study evaluated the RUSHU’s prognostic performance at six and 12 weeks in discriminating nonunion within a significantly larger cohort than before.

Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care.

Cite this article: Bone Joint J 2024;106-B(11):1206–1215.

Aims. Developmental cervical spinal stenosis (DcSS) is a well-known predisposing factor for degenerative cervical myelopathy (DCM) but there is a lack of consensus on its definition. This study aims to define DcSS based on MRI, and its multilevel characteristics, to assess the prevalence of DcSS in the general population, and to evaluate the presence of DcSS in the prediction of developing DCM. Methods. This cross-sectional study analyzed MRI spine morphological parameters at C3 to C7 (including anteroposterior (AP) diameter of spinal canal, spinal cord, and vertebral body) from DCM patients (n = 95) and individuals recruited from the general population (n = 2,019). Level-specific median AP spinal canal diameter from DCM patients was used to screen for stenotic levels in the population-based cohort. An individual with multilevel (≥ 3 vertebral levels) AP canal diameter smaller than the DCM median values was considered as having DcSS. The most optimal cut-off canal diameter per level for DcSS was determined by receiver operating characteristic analyses, and multivariable logistic regression was performed for the prediction of developing DCM that required surgery. Results. A total of 2,114 individuals aged 64.6 years (SD 11.9) who underwent surgery from March 2009 to December 2016 were studied. The most optimal cut-off canal diameters for DcSS are: C3 < 12.9 mm, C4 < 11.8 mm, C5 < 11.9 mm, C6 < 12.3 mm, and C7 < 13.3 mm. Overall, 13.0% (262 of 2,019) of the population-based cohort had multilevel DcSS. Multilevel DcSS (odds ratio (OR) 6.12 (95% CI 3.97 to 9.42); p < 0.001) and male sex (OR 4.06 (95% CI 2.55 to 6.45); p < 0.001) were predictors of developing DCM. Conclusion. This is the first MRI-based study for defining DcSS with multilevel canal narrowing. Level-specific cut-off canal diameters for DcSS can be used for early identification of individuals at risk of developing DCM. Individuals with DcSS at ≥ three levels and male sex are recommended for close monitoring or early intervention to avoid traumatic spinal cord injuries from stenosis. Cite this article: Bone Joint J 2024;106-B(11):1333–1341

Aims

The evidence base within trauma and orthopaedics has traditionally favoured quantitative research methodologies. Qualitative research can provide unique insights which illuminate patient experiences and perceptions of care. Qualitative methods reveal the subjective narratives of patients that are not captured by quantitative data, providing a more comprehensive understanding of patient-centred care. The aim of this study is to quantify the level of qualitative research within the orthopaedic literature.

Aims

The objective of this study was to compare simulated range of motion (ROM) for reverse total shoulder arthroplasty (rTSA) with and without adjustment for scapulothoracic orientation in a global reference system. We hypothesized that values for simulated ROM in preoperative planning software with and without adjustment for scapulothoracic orientation would be significantly different.

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

Aims

The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support.

Aims

This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously.

The aim was to predict the number and incidence of distal radius fractures in Scotland over the next two decades according to age group, categorised into under 65yrs(65) and 65yrs and older (65), and estimate the potential increased operative burden.

The number of distal radius fractures in Scotland was isolated from the Global Burden of Disease database and this was used, in addition to historic population data and published population estimates, to create a multivariable model allowing incorporation of age group, sex and time. A Negative Binomial distribution was used to predict incidence in 2030 and 2040 and calculate projected number of fractures. A 20.4% operative intervention rate was assumed (local data).

In terms of number of fractures, there was a projected 61% rise in the 65 group with an overall increase of 2099 fractures per year from 3417 in 2020 (95% confidence interval (CI) 2960 – 3463) to 5516 in 2040 (95% CI 4155 – 5675). This was associated with 428 additional operative interventions per year for those 65yrs. The projected increase between 2020 and 2040 was similar in both sexes (60% in females, 63% in males), however the absolute increase in fracture number was higher in females. There was a 4% projected fall in the number of fractures in those 65.

Incidence of distal radius fractures is expected to considerably increase over the next two decades due to a projected increase in the number of fractures in the elderly. This has implications for associated morbidity and healthcare resource use.

Aims

This study aims to evaluate the impact of metabolic syndrome in the setting of obesity on in-hospital outcomes and resource use after total joint replacement (TJR).

Aims

This study aimed to assess the risk of acute kidney injury (AKI) associated with combined intravenous (IV) and topical antibiotic therapy in patients undergoing treatment for periprosthetic joint infections (PJIs) following total knee arthroplasty (TKA), utilizing the Kidney Disease: Improving Global Outcomes (KDIGO) criteria for classification.

Aims. The risk factors for recurrent instability (RI) following a primary traumatic anterior shoulder dislocation (PTASD) remain unclear. In this study, we aimed to determine the rate of RI in a large cohort of patients managed nonoperatively after PTASD and to develop a clinical prediction model. Methods. A total of 1,293 patients with PTASD managed nonoperatively were identified from a trauma database (mean age 23.3 years (15 to 35); 14.3% female). We assessed the prevalence of RI, and used multivariate regression modelling to evaluate which demographic- and injury-related factors were independently predictive for its occurrence. Results. The overall rate of RI at a mean follow-up of 34.4 months (SD 47.0) was 62.8% (n = 812), with 81.0% (n = 658) experiencing their first recurrence within two years of PTASD. The median time for recurrence was 9.8 months (IQR 3.9 to 19.4). Independent predictors increasing risk of RI included male sex (p < 0.001), younger age at PTASD (p < 0.001), participation in contact sport (p < 0.001), and the presence of a bony Bankart (BB) lesion (p = 0.028). Greater tuberosity fracture (GTF) was protective (p < 0.001). However, the discriminative ability of the resulting predictive model for two-year risk of RI was poor (area under the curve (AUC) 0.672). A subset analysis excluding identifiable radiological predictors of BB and GTF worsened the predictive ability (AUC 0.646). Conclusion. This study clarifies the prevalence and risk factors for RI following PTASD in a large, unselected patient cohort. Although these data permitted the development of a predictive tool for RI, its discriminative ability was poor. Predicting RI remains challenging, and as-yet-undetermined risk factors may be important in determining the risk. Cite this article: Bone Joint J 2024;106-B(10):1111–1117

Aims

To explore key stakeholder views around feasibility and acceptability of trials seeking to prevent post-traumatic osteoarthritis (PTOA) following knee injury, and provide guidance for next steps in PTOA trial design.

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

Cite this article: Bone Joint Res 2024;13(9):507–512.