Aims

The aim of the LightFix Trial was to evaluate the clinical outcomes for one year after the treatment of impending and completed pathological fractures of the humerus using the IlluminOss System (IS), and to analyze the performance of this device.

Aims

Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments.

The February 2024 Oncology Roundup³⁶⁰ looks at: Does primary tumour resection improve survival for patients with sarcomas of the pelvis with metastasis at diagnosis?; Proximal femur replacements for an oncologic indication offer a durable endoprosthetic reconstruction option: a 40-year experience; The importance of awaiting biopsy results in solitary pathological proximal femoral fractures: do we need to biopsy solitary pathological fractures?; Effect of radiotherapy on local recurrence, distant metastasis, and overall survival in 1,200 extremity soft-tissue sarcoma patients; What to choose in bone tumour resections? Patient-specific instrumentation versus surgical navigation; Optimal timing of re-excision in synovial sarcoma patients: immediate intervention versus waiting for local recurrence; Survival differences of patients with resected extraskeletal osteosarcoma receiving two different (neo) adjuvant chemotherapy regimens; Solitary versus multiple bone metastases in the appendicular skeleton: should the surgical treatment be different?.

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.

Cite this article: Bone Joint J 2024;106-B(1):6–8.

Aims

Most patients with advanced malignancy suffer bone metastases, which pose a significant challenge to orthopaedic services and burden to the health economy. This study aimed to assess adherence to the British Orthopaedic Oncology Society (BOOS)/British Orthopaedic Association (BOA) guidelines on patients with metastatic bone disease (MBD) in the UK.

Aims. The modern prevalence of primary tumours causing metastatic bone disease is ill-defined in the oncological literature. Therefore, the purpose of this study is to identify the prevalence of primary tumours in the setting of metastatic bone disease, as well as reported rates of pathological fracture, postoperative complications, 90-day mortality, and 360-day mortality for each primary tumour subtype. Methods. The Premier Healthcare Database was queried to identify all patients who were diagnosed with metastatic bone disease from January 2015 to December 2020. The prevalence of all primary tumour subtypes was tabulated. Rates of long bone pathological fracture, 90-day mortality, and 360-day mortality following surgical treatment of pathological fracture were assessed for each primary tumour subtype. Patient characteristics and postoperative outcomes were analyzed based upon whether patients had impending fractures treated prophylactically versus treated completed fractures. Results. In total, 407,893 unique patients with metastatic bone disease were identified. Of the 14 primary tumours assessed, metastatic bone disease most frequently originated from lung (24.8%), prostatic (19.4%), breast (19.3%), gastrointestinal (9.4%), and urological (6.5%) malignancies. The top five malignant tumours resulting in long bone pathological fracture were renal (5.8%), myeloma (3.4%), female reproductive (3.2%), lung (2.8%), and breast (2.7%). Following treatment of pathological fractures of long bones, 90-day mortality rates were greatest for lung (12.1%), central nervous system (10.5%), lymphoma (10.4%), gastrointestinal (10.1%), and non-renal urinary (10.0%) malignancies. Finally, our study demonstrates improved 90-day and 360-day survival in patients treated for impending pathological fracture compared to completed fracture, as well as significantly lower rates of deep vein thrombosis, pulmonary embolism, urinary tract infection, and blood transfusion. Conclusion. This study defines the contemporary characteristics of primary malignancies resulting in metastatic bone disease. These data should be considered by surgeons when prognosticating patient outcomes during treatment of their metastatic bone disease. Cite this article: Bone Jt Open 2023;4(6):424–431

Aims

Obesity is associated with an increased risk of hip osteoarthritis, resulting in an increased number of total hip arthroplasties (THAs) performed annually. This study examines the peri- and postoperative outcomes of morbidly obese (MO) patients (BMI ≥ 40 kg/m²) compared to healthy weight (HW) patients (BMI 18.5 to < 25 kg/m²) who underwent a THA using the anterior-based muscle-sparing (ABMS) approach.

Long femoral nails for neck of femur fractures and prophylactic fixation have a risk of anterior cortex perforation. Previous studies have demonstrated the radius of curvature (ROC) of a femoral nail influencing the finishing point of a nail and the risk of anterior cortex perforation. This study aims to calculate a patients femoral ROC using preoperative XR and CT and therefore nail finishing position. We conducted a retrospective study review of patients with long femoral cephalomedullary nailing for proximal femur fractures (OTA/AO 31(A) and OTA/AO 32) or impending pathological fractures at a level 1 trauma centre between January 1, 2015 and December 31, 2020 with both full length lateral X-ray and CT imaging. Femoral ROC was calculated on both imaging modalities. Outcomes measured including nail finishing position, anterior cortex encroachment and impingement. The mean femoral ROC was 1026mm on CT and 1244mm on XR. CT femoral ROC strongly correlated with nail finishing point with a spearmans coefficient of 0.77. Additionally, femurs with a ROC <1000mm were associated with a higher risk of anterior encroachment (OR 6.12) and femurs with a ROC <900mm were associated with a higher risk of anterior cortex impingement (OR 6.47). To our knowledge this is the first study to compare a measured femoral ROC to nail finishing position. The use of CT to measure femoral ROC and to a lesser extent XR was able to predict both nail finishing position and risk of anterior cortex encroachment. Preoperative XRs and CTs were able to identify patients with a small femoral ROC. This predicted patients at risk of anterior cortex impingement, anterior cortex encroachment and nail finishing position. We may be able to select femoral nails that resemble the native femoral ROC and mitigate the risk of anterior cortex perforation

Aims

To review the evidence and reach consensus on recommendations for follow-up after total hip and knee arthroplasty.

The presence of metastatic bone disease (MBD) often necessitates major orthopaedic surgery. Patients will enter surgical care either through emergent or electively scheduled care pathways. Patients in a pain crisis or with an acute fracture are generally admitted via emergent care pathways whereas patients with identified high-risk bone lesions are often booked for urgent yet scheduled elective procedures. The purpose of this study is to compare the post-operative outcomes of patients who present through emergent or electively scheduled care pathways in patients in a Canadian health care system. We have conducted a retrospective, multicenter cohort study of all patients presenting for surgery for MBD of the femur, humerus, tibia or pelvis in southern Alberta between 2006 and 2021. Patients were identified by a search query of all patients with a diagnosis of metastatic cancer who underwent surgery for an impending or actual pathologic fracture in the Calgary, South and Central Alberta Zones. Subsequent chart reviews were performed. Emergent surgeries were defined by patients admitted to hospital via urgent care mechanisms and managed via unscheduled surgical bookings (“on call list”). Elective surgeries were defined by patients seen by an orthopaedic surgeon at least once prior to surgery, and booked for a scheduled urgent, yet elective procedure. Outcomes include overall survival from the time of surgery, hospital length of stay, and 30-day hospital readmission rate. We have identified 402 patients to date for inclusion. 273 patients (67.9%) underwent surgery through emergent pathways and 129 patients (32.1%) were treated through urgent, electively scheduled pathways. Lung, prostate, renal cell, and breast cancer were the most common primary malignancies and there was no significant difference in these primaries amongst the groups (p=0.06). Not surprisingly, emergent patients were more likely to be treated for a pathologic fracture (p<0.001) whereas elective patients were more likely to be treated for an impending fracture (p<0.001). Overall survival was significantly shorter in the emergent group (5.0 months, 95%CI: 4.0-6.1) compared to the elective group (14.9 months 95%CI: 10.4-24.6) [p<0.001]. Hospital length of stay was significantly longer in the emergent group (13 days, 95%CI: 12-16 versus 5 days, 95%CI: 5-7 days). There was a significantly greater rate of 30-day hospital readmission in the emergent group (13.3% versus 7.8%) [p=0.01]. Electively managed MBD has multiple benefits including longer post-operative survival, shorter length of hospital stay, and a lower rate of 30-day hospital readmission. These findings from a Canadian healthcare system demonstrate clinical value in providing elective orthopaedic care when possible for patients with MBD. Furthermore, care delivery interventions capable of decreasing the footprint of emergent surgery through enhanced screening or follow-up of patients with MBD has the potential to significantly improve clinical outcomes in this population. This is an ongoing study that will justify refinements to the current surgical care pathways for MBD in order to identify patients prior to emergent presentation. Future directions will evaluate the costs associated with each care delivery method to provide opportunity for health economic efficiencies

Surgical management for acute or impending pathologic fractures in metastatic bone disease (MBD) places patients at high-risk for post-operative venous thromboembolism (VTE). Due to the combination of malignancy, systemic cancer treatment, and surgical treatment, VTE-risk is increased 7-fold in patients with MBD compared to non-cancer patients undergoing the same procedure. The extent and duration of post-operative hypercoagulability in patients with MBD remains unknown and thromboprophylaxis guidelines were developed for non-cancer patients, limiting their applicability to address the elevated VTE-risk in cancer patients. Thrombelastography (TEG) analysis is a point-of-care test that measures clot formation, stabilization, and lysis in whole blood samples. The TEG parameter, maximal amplitude (MA), indicates clot strength and the threshold of ≥65 mm has been used to define hypercoagulability and predict VTE events in non-cancer patients requiring orthopaedic surgery. Therefore, this study aims to quantify the extent and duration of post-operative hypercoagulability in patients with MBD using serial TEG analysis. Consecutive adults (≥18 years) with MBD who required orthopaedic surgery for acute or impending pathologic fractures were enrolled into this single-centre, prospective cohort study. Serial TEG analysis was performed onsite using a TEG®6s haemostasis analyzer (Haemonetics Corporation, Boston, MA) on whole blood samples collected at seven timepoints: pre-operatively; on post-operative day (POD) 1, 3, and 5; and at 2-, 6-, and 12-weeks post-operatively. Hypercoagulability was defined as MA ≥65 mm. Participants received standardized thromboprophylaxis for four weeks and patient-reported compliance with thromboprophylaxis was recorded. VTE was defined as symptomatic DVT or PE, or asymptomatic proximal DVT, and all participants underwent a screening post-operative lower extremity Doppler ultrasound on POD3. Descriptive statistics were performed and difference between pre-operative MA values of participants with VTE versus no VTE was evaluated using Student's t-test (p≤0.05). Twenty-one participants (10 female; 47.6%) with a mean age of 70 ± 12 years were enrolled. Nine different primary cancers were identified amongst participants, with breast (23.8%), colorectal (19.0%), and lung cancer (14.3%) most frequently reported. Most participants (57.1%) were hypercoagulable pre-operatively, and nearly half remained hypercoagulable at 6- and 12-weeks post-operatively (47.1 and 46.7%, respectively). VTE occurred in 5 patients (23.8%) and mean MA was 68.1 ± 4.6 mm at the time of diagnosis. Mean pre-operative MA values were significantly higher (p=0.02) in patients who experienced VTE (68.9 ± 3.5 mm) compared to those who did not (62.7 ± 6.5 mm). VTE incidence was highest in the first week post-operatively, during which time four VTE events (80%) occurred. The proportion of patients in a hypercoagulable state increased at three consecutive timepoints, beginning on POD3 (85.0%), increasing on POD5 (87.5%), and peaking at 2-weeks post-operatively (88.9%). Current thromboprophylaxis guidelines do not consider cancer-associated risk factors that contribute to increased VTE incidence and prescription duration may be inadequate to address prolonged post-operative hypercoagulability in patients with MBD. The high rate of VTE events observed and sustained hypercoagulable state indicate that thromboprophylaxis may be prematurely terminated while patients remain at high risk for VTE. Therefore, extending thromboprophylaxis duration beyond 4-weeks post-operatively in patients with MBD warrants further investigation

Aims

The COVID-19 pandemic posed significant challenges to healthcare systems across the globe in 2020. There were concerns surrounding early reports of increased mortality among patients undergoing emergency or non-urgent surgery. We report the morbidity and mortality in patients who underwent arthroplasty procedures during the UK first stage of the pandemic.

Aims

The aim of this study was to assess orthopaedic oncologic patient morbidity resulting from COVID-19 related institutional delays and surgical shutdowns during the first wave of the pandemic in New York, USA.

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

Aims

Restarting planned surgery during the COVID-19 pandemic is a clinical and societal priority, but it is unknown whether it can be done safely and include high-risk or complex cases. We developed a Surgical Prioritization and Allocation Guide (SPAG). Here, we validate its effectiveness and safety in COVID-free sites.

Aims

Preoperative talar valgus deformity ≥ 15° is considered a contraindication for total ankle arthroplasty (TAA). We compared operative procedures and clinical outcomes of TAA in patients with talar valgus deformity ≥ 15° and < 15°.

Advances in cancer therapy have prolonged patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL). Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in longer survival, preserved mobility, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The ideal clinical decision support tool will be of the highest sensitivity and specificity, non-invasive, generalizable to all patients, and not a burden on hospital resources or the patient's time. Our research uses novel machine learning techniques to develop a model to fill this considerable gap in the treatment pathway of MBD of the femur. The goal of our study is to train a convolutional neural network (CNN) to predict fracture risk when metastatic bone disease is present in the proximal femur. Our fracture risk prediction tool was developed by analysis of prospectively collected data of consecutive MBD patients presenting from 2009–2016. Patients with primary bone tumors, pathologic fractures at initial presentation, and hematologic malignancies were excluded. A total of 546 patients comprising 114 pathologic fractures were included. Every patient had at least one Anterior-Posterior X-ray and clinical data including patient demographics, Mirel's criteria, tumor biology, all previous radiation and chemotherapy received, multiple pain and function scores, medications and time to fracture or time to death. We have trained a convolutional neural network (CNN) with AP X-ray images of 546 patients with metastatic bone disease of the proximal femur. The digital X-ray data is converted into a matrix representing the color information at each pixel. Our CNN contains five convolutional layers, a fully connected layers of 512 units and a final output layer. As the information passes through successive levels of the network, higher level features are abstracted from the data. The model converges on two fully connected deep neural network layers that output the risk of fracture. This prediction is compared to the true outcome, and any errors are back-propagated through the network to accordingly adjust the weights between connections, until overall prediction accuracy is optimized. Methods to improve learning included using stochastic gradient descent with a learning rate of 0.01 and a momentum rate of 0.9. We used average classification accuracy and the average F1 score across five test sets to measure model performance. We compute F1 = 2 x (precision x recall)/(precision + recall). F1 is a measure of a model's accuracy in binary classification, in our case, whether a lesion would result in pathologic fracture or not. Our model achieved 88.2% accuracy in predicting fracture risk across five-fold cross validation testing. The F1 statistic is 0.87. This is the first reported application of convolutional neural networks, a machine learning algorithm, to this important Orthopaedic problem. Our neural network model was able to achieve reasonable accuracy in classifying fracture risk of metastatic proximal femur lesions from analysis of X-rays and clinical information. Our future work will aim to externally validate this algorithm on an international cohort

Advances in cancer therapy have prolonged cancer patient survival even in the presence of disseminated disease and an increasing number of cancer patients are living with metastatic bone disease (MBD). The proximal femur is the most common long bone involved in MBD and pathologic fractures of the femur are associated with significant morbidity, mortality and loss of quality of life (QoL). Successful prophylactic surgery for an impending fracture of the proximal femur has been shown in multiple cohort studies to result in patients more likely to walk after surgery, longer survival, lower transfusion rates and shorter post-operative hospital stays. However, there is currently no optimal method to predict a pathologic fracture. The most well-known tool is Mirel's criteria, established in 1989 and is limited from guiding clinical practice due to poor specificity and sensitivity. The goal of our study is to train a convolutional neural network (CNN) to predict fracture risk when metastatic bone disease is present in the proximal femur. Our fracture risk prediction tool was developed by analysis of prospectively collected data for MBD patients (2009–2016) in order to determine which features are most commonly associated with fracture. Patients with primary bone tumors, pathologic fractures at initial presentation, and hematologic malignancies were excluded. A total of 1146 patients comprising 224 pathologic fractures were included. Every patient had at least one Anterior-Posterior X-ray. The clinical data includes patient demographics, tumor biology, all previous radiation and chemotherapy received, multiple pain and function scores, medications and time to fracture or time to death. Each of Mirel's criteria has been further subdivided and recorded for each lesion. We have trained a convolutional neural network (CNN) with X-ray images of 1146 patients with metastatic bone disease of the proximal femur. The digital X-ray data is converted into a matrix representing the color information at each pixel. Our CNN contains five convolutional layers, a fully connected layers of 512 units and a final output layer. As the information passes through successive levels of the network, higher level features are abstracted from the data. This model converges on two fully connected deep neural network layers that output the fracture risk. This prediction is compared to the true outcome, and any errors are back-propagated through the network to accordingly adjust the weights between connections. Methods to improve learning included using stochastic gradient descent with a learning rate of 0.01 and a momentum rate of 0.9. We used average classification accuracy and the average F1 score across test sets to measure model performance. We compute F1 = 2 x (precision x recall)/(precision + recall). F1 is a measure of a test's accuracy in binary classification, in our case, whether a lesion would result in pathologic fracture or not. Five-fold cross validation testing of our fully trained model revealed accurate classification for 88.2% of patients with metastatic bone disease of the proximal femur. The F1 statistic is 0.87. This represents a 24% error reduction from using Mirel's criteria alone to classify the risk of fracture in this cohort. This is the first reported application of convolutional neural networks, a machine learning algorithm, to an important Orthopaedic problem. Our neural network model was able to achieve impressive accuracy in classifying fracture risk of metastatic proximal femur lesions from analysis of X-rays and clinical information. Our future work will aim to validate this algorithm on an external cohort

Aims

Enhanced perioperative protocols have significantly improved patient recovery following primary total knee arthroplasty (TKA). Little has been investigated the effectiveness of these protocols for revision TKA (RTKA). We report on a matched group of aseptic revision and primary TKA patients treated with an identical pain and rehabilitation programmes.