Over 300,000 total hip arthroplasties (THA) are performed annually in the USA. Surgical Site Infections (SSI) are one of the most common complications and are associated with increased morbidity, mortality and cost. Risk factors for SSI include obesity, diabetes and smoking, but few studies have reported on the predictive value of pre-operative blood markers for SSI. The purpose of this study was to create a clinical prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of THA based on commonly ordered pre-operative lab markers and using data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral THA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was used to determine coefficients, with manual stepwise reduction. Receiver Operating Characteristic (ROC) curves were also graphed. The SSI prediction model included the following covariates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphate, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. Since the data met logistic assumption requirements, bootstrap estimation was used to measure internal validity. The area under the ROC curve for final derivations along with McFadden's R-squared were utilized to compare prediction models. A total of 130,619 patients were included with the median age of patients at time of THA was 67 years (mean=66.6+11.6 years) with 44.8% (n=58,757) being male. A total of 1,561 (1.20%) patients had a superficial or deep SSI (overall SSI). Of all SSI, 45.1% (n=704) had a deep SSI and 55.4% (n=865) had a superficial SSI. The incidence of SSI occurring annually decreased from 1.44% in 2011 to 1.16% in 2016. Area under the ROC curve for the SSI prediction model was 0.79 and 0.78 for deep and superficial SSI, respectively and 0.71 for overall SSI. CHF had the largest effect size (Odds Ratio(OR)=2.88, 95% Confidence Interval (95%CI): 1.56 – 5.32) for overall SSI risk. Albumin (OR=0.44, 95% CI: 0.37 – 0.52, OR=0.31, 95% CI: 0.25 – 0.39, OR=0.48, 95% CI: 0.41 – 0.58) and sodium (OR=0.95, 95% CI: 0.93 – 0.97, OR=0.94, 95% CI: 0.91 – 0.97, OR=0.95, 95% CI: 0.93 – 0.98) levels were consistently significant in all clinical prediction models for superficial, deep and overall SSI, respectively. In terms of pre-operative blood markers, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. In this large NSQIP database study, we were able to create an SSI prediction model and identify risk factors for predicting acute superficial, deep and overall SSI after THA. To our knowledge, this is the first clinical model whereby pre-operative hyponatremia (in addition to hypoalbuminemia) levels have been predictive of SSI after THA. Although the model remains without external validation, it is a vital starting point for developing a risk prediction model for SSI and can help physicians mitigate risk factors for acute SSI post THA

Total knee arthroplasty (TKA) is the most commonly performed elective orthopaedic procedure. With an increasingly aging population, the number of TKAs performed is expected to be ∼2,900 per 100,000 by 2050. Surgical Site Infections (SSI) after TKA can have significant morbidity and mortality. The purpose of this study was to construct a risk prediction model for acute SSI (classified as either superficial, deep and overall) within 30 days of a TKA based on commonly ordered pre-operative blood markers and using audited administrative data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. All adult patients undergoing an elective unilateral TKA for osteoarthritis from 2011–2016 were identified from the NSQIP database using Current Procedural Terminology (CPT) codes. Patients with active or chronic, local or systemic infection/sepsis or disseminated cancer were excluded. Multivariate logistic regression was conducted to estimate coefficients, with manual stepwise reduction to construct models. Bootstrap estimation was administered to measure internal validity. The SSI prediction model included the following co-variates: body mass index (BMI) and sex, comorbidities such as congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), smoking, current/previous steroid use, as well as pre-operative blood markers, albumin, alkaline phosphatase, blood urea nitrogen (BUN), creatinine, hematocrit, international normalized ratio (INR), platelets, prothrombin time (PT), sodium and white blood cell (WBC) levels. To compare clinical models, areas under the receiver operating characteristic (ROC) curves and McFadden's R-squared values were reported. The total number of patients undergoing TKA were 210,524 with a median age of 67 years (mean age of 66.6 + 9.6 years) and the majority being females (61.9%, N=130,314). A total of 1,674 patients (0.8%) had a SSI within 30 days of the index TKA, of which N=546 patients (33.2%) had a deep SSI and N=1,128 patients (67.4%) had a superficial SSI. The annual incidence rate of overall SSI decreased from 1.60% in 2011 to 0.68% in 2016. The final risk prediction model for SSI contained, smoking (OR=1.69, 95% CI: 1.31 – 2.18), previous/current steroid use (OR=1.66, 95% CI: 1.23 – 2.23), as well as the pre-operative lab markers, albumin (OR=0.46, 95% CI: 0.37 – 0.56), blood urea nitrogen (BUN, OR=1.01, 95% CI: 1 – 1.02), international normalized ratio (INR, OR=1.22, 95% CI:1.05 – 1.41), and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98;). Area under the ROC curve for the final model of overall SSI was 0.64. Models for deep and superficial SSI had ROC areas of 0.68 and 0.63, respectively. Albumin (OR=0.46, 95% CI: 0.37 – 0.56, OR=0.33, 95% CI: 0.27 – 0.40, OR=0.75, 95% CI: 0.59 – 0.95) and sodium levels (OR=0.94, 95% CI: 0.91 – 0.98, OR=0.96, 95% CI: 0.93 – 0.99, OR=0.97, 95% CI: 0.96 – 0.99) levels were consistently significant in all prediction models for superficial, deep and overall SSI, respectively. Overall, hypoalbuminemia and hyponatremia are both significant risk factors for superficial, deep and overall SSI. To our knowledge, this is the first prediction model for acute SSI post TKA whereby hyponatremia (and hypoalbuminemia) are predictive of SSI. This prediction model can help fill an important gap for predicting risk factors for SSI after TKA and can help physicians better optimize patients prior to TKA

Fractures through the physis account for 18–30% of all paediatric fractures, leading to growth arrest in 5.5% of cases. We have limited knowledge to predict which physeal fractures result in growth arrest and subsequent deformity or limb length discrepancy. The purpose of this study is to identify factors associated with physeal growth arrest to improve patient outcomes. This prospective cohort study was designed to develop a clinical prediction model for growth arrest after physeal injury. Patients < 1 8 years old presenting within four weeks of injury were enrolled if they had open physes and sustained a physeal fracture of the humerus, radius, ulna, femur, tibia or fibula. Patients with prior history of same-site fracture or a condition known to alter bone growth or healing were excluded. Demographic data, potential prognostic indicators and radiographic data were collected at baseline, one and two years post-injury. A total of 167 patients had at least one year of follow-up. Average age at injury was 10.4 years, 95% CI [9.8,10.94]. Reduction was required in 51% of cases. Right-sided (52.5%) and distal (90.1%) fractures were most common. After initial reduction 52.5% of fractures had some form of residual angulation and/or displacement (38.5% had both). At one year follow-up, 34 patients (21.1%) had evidence of a bony bridge on plain radiograph, 10 (6.2%) had residual angulation (average 12.6°) and three had residual displacement. Initial angulation (average 22.4°) and displacement (average 5.8mm) were seen in 16/34 patients with bony bridge (48.5%), with 10 (30.3%) both angulated and displaced. Salter-Harris type II fractures were most common across all patients (70.4%) and in those with bony bridges (57.6%). At one year, 44 (27.3%) patients had evidence of closing/closed physes. At one year follow-up, there was evidence of a bony bridge across the physis in 21.1% of patients on plain film, and residual angulation and/or displacement in 8.1%. Initial angulation and/or displacement was present in 64.7% of patients showing possible evidence of growth arrest. The incidence of growth arrest in this patient population appears higher than past literature reports. However, plain film is an unreliable modality for assessing physeal bars and the true incidence may be lower. A number of patients were approaching skeletal maturity at time of injury and any growth arrest is likely to have less clinical significance in these cases. Further prospective long-term follow-up is required to determine the true incidence and impact of growth arrest

Introduction. Previous studies have shown that third body damage to the femoral head in metal-on-polyethylene hip replacement bearings can lead to accelerated wear of the polyethylene liners. The resulting damage patterns observed on retrieved metal heads are typically scratches and scrapes. The damage created in vitro must represent the third body damage that occurs clinically. A computational model was developed to predict the acceleration of wear of polyethylene articulating against in vitro damaged femoral heads. This involved using a damage registry from retrieval femoral heads to develop standardized templates of femoral head scratches statistically representative of retrieval damage. The aim of this study was to determine the wear rates of polyethylene liners articulating against retrievals and artificially damaged metal heads for the purpose of validating a computational wear prediction model; and to develop and validate an in vitro standardised femoral head damage protocol for pre-clinical testing of hip replacements. Materials and Methods. Twenty nine, 32mm diameter, metal-on-moderately cross-linked polyethylene bearings (Marathon. TM. ) inserted into Ti-6Al-4V shells (Pinnacle. ®). were tested in this study. All products were manufactured by DePuy Synthes, Warsaw, Indiana, USA. Following a retrieval study seven different damage patterns were defined, and these were applied to the femoral heads using a four-degree-of-freedom CNC milling machine (Figure 1). The ProSim 10-station pneumatic hip joint simulator (Simulation Solutions, UK) was used for experimental wear simulation using standard gait cycles and testing each experimental group for 3 million cycles. The acetabular cups were inclined at 35° on the simulator (equivalent to 45° in vivo). The wear volumes were determined using a microbalance (Mettler-Toledo XP205, Switzerland) at one million cycle intervals. Statistical analysis used was one way ANOVA followed by a post hoc analysis with significance taken at p<0.05. Results. Different damage patterns accelerated the wear of polyethylene at different rates (Figure 2). The moderately scratched and severely scratched heads caused a 2 fold (p<0.01) and 5.5 fold (p<0.01) increase when compared to the wear rate of the undamaged head group. However, the scraped damage caused a lower increase than the scratched heads, with a 1.4 fold (p=0.2) increase for the moderately scraped heads and 2.6 fold (p<0.01) increase for the severely scraped heads. The moderate hybrid and severe hybrid groups resulted in a similar increase to the scraped heads with 1.8 fold (p<0.01) increase with the moderate hybrid and 3 fold (p<0.01) increase with the severe hybrid. The wear of polyethylene against the mild hybrid and retrieved heads was not significantly different (p= 0.9) to the wear against undamaged heads. Discussion. A standardised protocol for generating in vitro damage representative of clinically occurring damage on femoral heads for preclinical testing purposes is needed. The wear rates of polyethylene liners articulating against the retrieval heads were similar to those articulating against the undamaged femoral heads. This study has shown the variations in wear rate of polyethylene bearing under different damage patterns generated in vitro. The wear prediction computational model predict similar trends of the wear acceleration reported in the experimental study

Numerous prediction tools are available for estimating postoperative risk following spine surgery. External validation studies have shown mixed results. We present the development, validation, and comparative evaluation of novel tool (NZSpine) for modelling risk of complications within 30 days of spine surgery. Data was gathered retrospectively from medical records of patients who underwent spine surgery at Waikato Hospital between January 2019 and December 2020 (n = 488). Variables were selected a priori based on previous evidence and clinical judgement. Postoperative adverse events were classified objectively using the Comprehensive Complication Index. Models were constructed for the occurrence of any complication and significant complications (based on CCI >26). Performance and clinical utility of the novel model was compared against SpineSage (. https://depts.washington.edu/spinersk/. ), an extant online tool which we have shown in unpublished work to be valid in our local population. Overall complication rate was 34%. In the multivariate model, higher age, increased surgical invasiveness and the presence of preoperative anemia were most strongly predictive of any postoperative complication (OR = 1.03, 1.09, 2.1 respectively, p <0.001), whereas the occurrence of a major postoperative complication (CCI >26) was most strongly associated with the presence of respiratory disease (OR = 2.82, p <0.001). Internal validation using the bootstrapped models showed the model was robust, with an AUC of 0.73. Using sensitivity analysis, 80% of the model's predictions were correct. By comparison SpineSage had an AUC of 0.71, and in decision curve analysis the novel model showed greater expected benefit at all thresholds of risk. NZSpine is a novel risk assessment tool for patients undergoing acute and elective spine surgery and may help inform clinicians and patients of their prognosis. Use of an objective tool may help to provide uniformity between DHBs when completing the “clinician assessment of risk” section of the national prioritization tool

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

Aim. Recurrence of bone and joint infection, despite appropriate therapy, is well recognised and stimulates ongoing interest in identifying host factors that predict infection recurrence. Clinical prediction models exist for those treated with DAIR, but to date no models with a low risk of bias predict orthopaedic infection recurrence for people with surgically excised infection and removed metalwork. The aims of this study were to construct and internally validate a risk prediction model for infection recurrence at 12 months, and to identify factors that predict recurrence. Predictive factors must be easy to check in pre-operative assessment and relevant across patient groups. Methods. Four prospectively collected datasets including 1173 participants treated in European centres between 2003 and 2021, followed up to 12 months after surgery for orthopaedic infections, were included in logistic regression modelling [1–3]. The definition of infection recurrence was identical and ascertained separately from baseline factors in three contributing cohorts. Eight predictive factors were investigated following a priori sample size calculation: age, gender, BMI, ASA score, the number of prior operations, immunosuppressive medication, glycosylated haemoglobin (HbA1c), and smoking. Missing data, including systematically missing predictors, were imputed using Multiple Imputation by Chained Equations. Weekly alcohol intake was not included in modelling due to low inter-observer reliability (mean reported intake 12 units per week, 95% CI for mean inter-rater error −16.0 to +15.4 units per week). Results. Participants were 64% male, with a median age of 60 years (range 18–95). 86% of participants had lower limb orthopaedic infections. 732 participants were treated for osteomyelitis, including FRI, and 432 for PJI. 16% of participants experienced treatment failure by 12 months. The full prediction model had moderate apparent discrimination: AUROC (C statistic) 0.67, Brier score 0.13, and reasonable apparent calibration. Of the predictors of interest, associations with failure were seen with prior operations at the same anatomical site (odds ratio for failure 1.51 for each additional prior surgery; 95% CI 1.02 to 2.22, p=0.06), and the current use of immunosuppressive medications (odds ratio for failure 2.94; 95% CI 0.89 to 9.77, p=0.08). Conclusions. This association between number of prior surgeries and treatment failure supports the urgent need to streamline referral pathways for people with orthopaedic infection to specialist multidisciplinary units

To explore a novel machine learning model to evaluate the vertebral fracture risk using Decision Tree model and train the model by Bone Mineral Density (BMD) of different compartments of vertebral body. We collected a Computed Tomography image dataset, including 10 patients with osteoporotic fracture and 10 patients without osteoporotic fracture. 40 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients with osteoporotic fracture in the CT database and 53 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients without osteoporotic fracture in the CT database. Based on the biomechanical properties, 93 vertebral bodies were further segmented into 11 compartments: eight trabecular bone, cortical shell, top and bottom endplate. BMD of these 11 compartments was calculated based on the HU value in CT images. Decision tree model was used to build fracture prediction model, and Support Vector Machine was built as a compared model. All BMD data was shuffled to a random order. 70% of data was used as training data, and 30% left was used as test data. Then, training prediction accuracy and testing prediction accuracy were calculated separately in the two models. The training accuracy of Decision Tree model is 100% and testing accuracy is 92.14% after trained by BMD data of 11 compartments of the vertebral body. The type I error is 7.14% and type II error is 0%. The training accuracy of Support Vector Machine model is 100% and the testing accuracy is 78.57%. The type I error is 17.86% and type II error is 3.57%. The performance of vertebral body fracture prediction using Decision Tree is significantly higher than using Support Vector Machine. The Decision Tree model is a potential risk assessment method for clinical application. The pilot evidence showed that Decision Tree prediction model overcomes the overfitting drawback of Support Vector Machine Model. However, larger dataset and cohort study should be conducted for further evidence

Source of the study: University of Auckland, Auckland, New Zealand and University of Otago, Christchurch, New Zealand. The Oxford Knee Score (OKS) is a 12-item questionnaire used to track knee arthroplasty outcomes. Validation of such patient reported outcome measures is typically anchored to a single question based on patient ‘satisfaction’, however risk of subsequent revision surgery is also an important outcome measure. The OKS can predict subsequent revision risk within two years, however it is not known which item(s) are the strongest predictors. Our aim was to identify which questions were most relevant in the prediction of subsequent knee arthroplasty revision risk. . All primary TKAs (n=27,708) and UKAs (n=8,415) captured by the New Zealand Joint Registry between 1999 and 2019 with at least one OKS response at six months, five years or ten years post-surgery were included. Logistic regression and receiver operating characteristics (ROC) curves were used to assess prediction models at six months, five years and ten years. Q1 ‘overall pain’ was the strongest predictor of revision within two years (TKA: 6 months, odds ratio (OR) 1.37; 5 years, OR 1.80; 10 years, OR 1.43; UKA: 6 months, OR 1.32; 5 years, OR 2.88; 10 years, OR 1.85; all p<0.05). A reduced model with just three questions (Q1, Q6 ‘limping when walking’, Q10 ‘knee giving way’) showed comparable or better diagnostic ability with the full OKS (area under the curve (AUC): TKA: 6 months, 0.77 vs. 0.76; 5 years, 0.78 vs. 0.75; 10 years, 0.76 vs. 0.73; UKA: 6 months, 0.80 vs. 0.78; 5 years: 0.81 vs. 0.77; 10 years, 0.80 vs. 0.77). The three questions on overall knee pain, limping when walking, and knee ‘giving way’ were the strongest predictors of subsequent revision within two years. Attention to the responses for these three key questions during follow-up may allow for prompt identification of patients most at risk of revision

Introduction. Diaphyseal tibial fractures account for approximately 1.9% of adult fractures. Several studies demonstrate a high proportion of diaphyseal tibial fractures have ipsilateral occult posterior malleolus fractures, this ranges from 22–92.3%. Materials and Methods. A retrospective review of a prospectively collected database was performed at Liverpool University Hospitals NHS Foundation Trust between 1/1/2013 and 9/11/2020. The inclusion criteria were patients over 16, with a diaphyseal tibial fracture and who underwent a CT. The articular fracture extension was categorised into either posterior malleolar (PM) or other fracture. Results. 764 fractures were analysed, 300 had a CT. There were 127 intra-articular fractures. 83 (65.4%) cases were PM and 44 were other fractures. On univariate analysis for PM fractures, fibular spiral (p=.016) fractures, no fibular fracture(p=.003), lateral direction of the tibial fracture (p=.04), female gender (p=.002), AO 42B1 (p=.033) and an increasing angle of tibial fracture. On multivariate regression analysis a high angle of tibia fracture was significant. Other fracture extensions were associated with no fibular fracture (p=.002), medial direction of tibia fracture (p=.004), female gender (p=.000), and AO 42A1 (p=.004), 42A2 (p=.029), 42B3 (p=.035) and 42C2 (p=.032). On multivariate analysis, the lateral direction of tibia fracture, and AO classification 42A1 and 42A2 were significant. Conclusions. Articular extension happened in 42.3%. A number of factors were associated with the extension, however multivariate analysis did not create a suitable prediction model. Nevertheless, rotational tibia fractures with a high angle of fracture should have further investigation with a CT

Introduction. A total knee replacement is a proven cost-effective treatment for end-stage osteoarthritis, with a positive effect on pain and function. However, only 80% of the patients are satisfied after surgery. It is known that high preoperative expectations and residual postoperative pain are important determinants of satisfaction, but also malalignment, poor function and disturbed kinematics can be a cause. The purpose of this study was to investigate the correlation between the preoperative function and the postoperative patient reported outcomes PROMs) as well as the influence of the postoperative functional rehabilitation on the PROMs. Methods. 57 patients (mean 62,9j ± 10,6j), who suffer from knee osteoarthritis and who were scheduled for a total knee replacement at our centre, participated in this study. The range of motion of the knee, the muscle strength of the M. Quadriceps and the M. Hamstrings and the functional parameters (‘stair climbing test’ (SCT), ‘Sit to stand’ (STS) and ‘6 minutes walking test’ (6MWT)) were measured the night before surgery, ±6 months and ±1 year after surgery. This happened respectively with the use of a goniometer, HHD 2, stopwatch and the ‘DynaPort Hybrid’. Correlations between pre- and postoperative values were investigated. Secondly, a prediction was made about the influence of the preoperative parameters on on the subjective questionnaires (KOOS, OXFORD and KSS) as well as a linear and logistic regression. Results. 6 Months after surgery, an improvement of all parameters for ROM, muscle strength and functional status was found. With a significant difference for the active and passive ROM toward knee flexion (p=0.007;p=0.008), asymmetry in active and passive ROM toward flexion between the healthy leg and the leg with the TKA (p=0.001;p=0.001), Quadriceps- and Hamstrings strength (p=0.001;p<0.001), time of the STS test (p=0.012), time sit-stand (p=0.002), time stand-sit (p=0.001;p<0.001), all parameters for the 6MWT and the time of the SCT (p=0.001). Regarding the prediction model, the 6month PROMs can be predicted by some parameters for the 6MWT (distance (p=0.001), gait steps (p=0.002) and step time TKA (p=0.007)). These parameters are predictors for the score on the subscales ‘symptoms’ and ‘pain’ of the KOOS questionnaire. 1 Year after surgery, there is an improvement of all parameters, except for the active and passive ROM toward knee extension. However, these differences are not significant. The 1 year PROMs can only be predicted by the muscle strength (Quadriceps- and Hamstrings strength (p=0.026; p=0.039) and the asymmetry in Quadriceps strength between the healthy leg and the leg with the TKA (p=0.031)). The score on the subscale ‘pain’ can be predicted based on the parameters mentioned above. Conclusion. Patient satisfaction after TKA is a multivariate model. Regarding the functional outcome, we could find that there is a correlation between the muscle force, walking distance and the PROMs. More research is currently being done to create a better prediction model and investigate the correlations more thoroughly

While total joint replacement (TJR) is considered as an effective intervention to relieve pain and restore joint function for end-stage osteoarthritis (OA) patients, a significant proportion of the patients are dissatisfied with their surgery outcomes. The aim of this study was to identify genetic factors that can predict patients who do or do not benefit from these surgical procedures by a genome-wide association study (GWAS). Study participants were derived from the Newfoundland Osteoarthritis Study (NFOAS) which consisted of 1086 TJR patients. Non-responders to TJR was defined as patients who did not reach the minimum clinically important difference (MCID) based on the self administered Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) in terms of pain reduction or function improvment. DNA was extracted from the blood samples of the study participants and genotyped by Illumina GWAS genotyping platform. Over two million single nucleotide polymorphisms (SNPs) across the genome were genotyped and tested for assocition with non-responders. 39 non-responders and 44 age, sex, and BMI matched responders were included in this study. Four chromosome regions on chromosomes 5, 7, 8, and 12 were suggested to be associated with non-responders with p < 1 0–5. The most promising one was on chromosome 5 with the lead SNP rs17118094 (p=1.7×10–6) which can classify 72% of non-responders accurately. The discriminatory power of this SNP alone is very promising as indicated by an area under the curve (AUC) of 0.72 with 95% confidence interval of 0.63 to 0.81, which is much better than any previously studied predictors mentioned above. All the patients who carry two copies of the G allele (minor allele) of rs17118094 were non-responders and 75% of those who carry one copy of the G allele were non-responders. The discriminatory ability of the lead SNPs on chromosomes 7 and 12 were comparable to the one on chromosome 5 with an AUC of 0.74, and 88% of patients who carry two copies of the A allele of rs10244798 on chromosome 7 were non-responders. Similarly, 88% of patients who carry two copies of the C allele of rs10773476 on chromosome 12 were non-responders. While the discriminatory ability of rs9643244 on chromosome 8 was poor with an AUC of 0.26, its strong association with non-responders warrants a further investigation in the region. The study identified four genomic regions harboring genetic factors for non-responders to TJR. The lead SNPs in those regions have great discriminatory ability to predict non-responders and could be used to create a genetic prediction model for clinical unitilty and application

Introduction. Up to 60% of total hip arthroplasties (THA) in Asian populations arise from avascular necrosis (AVN), a bone disease that can lead to femoral head collapse. Current diagnostic methods to classify AVN have poor reproducibility and are not reliable in assessing the fracture risk. Femoral heads with an immediate fracture risk should be treated with a THA, conservative treatments are only successful in some cases and cause unnecessary patient suffering if used inappropriately. There is potential to improve the assessment of the fracture risk by using a combination of density-calibrated computed tomographic (QCT) imaging and engineering beam theory. The aim of this study was to validate the novel fracture prediction method against in-vitro compression tests on a series of six human femur specimens. Methods. Six femoral heads from six subjects were tested, a subset (n=3) included a hole drilled into the subchondral area of the femoral head via the femoral neck (University of Leeds, ethical approval MEEC13-002). The simulated lesions provided a method to validate the fracture prediction model with respect of AVN. The femoral heads were then modelled by a beam loaded with a single joint contact load. Material properties were assigned to the beam model from QCT-scans by using a density-modulus relationship. The maximum joint loading at which each bone cross-section was likely to fracture was calculated using a strain based failure criterion. Based on the predicted fracture loads, all six femoral heads (validation set) were classified into two groups, high fracture risk and low fracture risk (Figure 1). Beam theory did not allow for an accurate fracture load to be found because of the geometry of the femoral head. Therefore the predicted fracture loads of each of the six femoral heads was compared to the mean fracture load from twelve previously analysed human femoral heads (reference set) without lesions. The six cemented femurs were compression tested until failure. The subjects with a higher fracture risk were identified using both the experimental and beam tool outputs. Results. The computational tool correctly identified all femoral head samples which fractured at a significantly low load in-vitro (Figure 2). Both samples with a low experimental fracture load had an induced lesion in the subchondral area (Figure 3). Discussion. This study confirmed findings of a previous verification study on a disease models made from porcine femoral heads (Preutenborbeck et al. I-CORS2016). It demonstrated that fracture prediction based on beam theory is a viable tool to predict fracture. The tests confirmed that samples with a lesion in the weight bearing area were more likely to fracture at a low load however not all samples with a lesion fractured with a low load experimentally, indicating that a lesion alone is not a sufficient factor to predict fracture. The developed tool takes both structural and material properties into account when predicting the fracture risk. Therefore it might be superior to current diagnostic methods in this respect and it has the added advantage of being largely automated and therefore removing the majority of user bias. For any figures or tables, please contact the authors directly

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.

Aims

The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.