Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Aims

A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.

Acute bone and joint infections in children are serious, and misdiagnosis can threaten limb and life. Most young children who present acutely with pain, limping, and/or loss of function have transient synovitis, which will resolve spontaneously within a few days. A minority will have a bone or joint infection. Clinicians are faced with a diagnostic challenge: children with transient synovitis can safely be sent home, but children with bone and joint infection require urgent treatment to avoid complications. Clinicians often respond to this challenge by using a series of rudimentary decision support tools, based on clinical, haematological, and biochemical parameters, to differentiate childhood osteoarticular infection from other diagnoses. However, these tools were developed without methodological expertise in diagnostic accuracy and do not consider the importance of imaging (ultrasound scan and MRI). There is wide variation in clinical practice with regard to the indications, choice, sequence, and timing of imaging. This variation is most likely due to the lack of evidence concerning the role of imaging in acute bone and joint infection in children. We describe the first steps of a large UK multicentre study, funded by the National Institute for Health Research, which seeks to integrate definitively the role of imaging into a decision support tool, developed with the assistance of individuals with expertise in the development of clinical prediction tools. Cite this article: Bone Joint J 2023;105-B(3):227–229

AO Spine Guideline for Using Osteobiologics in Spine Degeneration project is an international collaborative initiative to identify and evaluate evidence on existing use of osteobiologics in spine degenerative diseases. It aims to formulate clinically relevant and internationally applicable guidelines ensuring evidence-based, safe and effective use of osteobiologics. The current focus is the use of osteobiologics in anterior cervical discectomy and fusion surgeries. The guideline development is planned in three phases. Phase 1- Evidence synthesis and Recommendation; Phase 2- Guideline with osteobiologics grading and Validation; Phase 3- Guideline dissemination and Development of a clinical decision support tool. The key questions formulating the guidelines for the use of osteobiologics will be addressed in a series of systematic reviews in Phase 1. The evidence synthesized by the systematic reviews will be assessed by Grading of Recommendations, Assessment, Development and Evaluations (GRADE) methodology, including expert panel discussions to formulate a recommendation. In Phase 2, osteobiologics will be graded based on evidence and the grading will be integrated with the recommendation from Phase 1, and thus formulate a guideline. The guideline will be further validated by prospective clinical studies. In the third phase, dissemination of the proposed guideline and development of a decision support tool is planned. AO-GO aims to bridge an important gap between quality of evidence and use of osteobiologics in spine fusion surgeries. With a holistic approach the guideline aims to facilitate evidence-based, patient-oriented decision-making process in clinical practice, thus stimulating further evidence-based studies regarding osteobiologics usage in spine surgeries

Introduction. Clinical decision support tools are software that match the input characteristics of an individual patient to an established knowledge base to create patient-specific assessments that support and better inform individualized healthcare decisions. Clinical decision support tools can facilitate better evidence-based care and offer the potential for improved treatment quality and selection, shared decision making, while also standardizing patient expectations. Methods. Predict+ is a novel, clinical decision support tool that leverages clinical data from the Exactech Equinoxe shoulder clinical outcomes database, which is composed of >11,000 shoulder arthroplasty patients using one specific implant type from more than 30 different clinical sites using standardized forms. Predict+ utilizes multiple coordinated and locked supervised machine learning algorithms to make patient-specific predictions of 7 outcome measures at multiple postoperative timepoints (from 3 months to 7 years after surgery) using as few as 19 preoperative inputs. Predict+ algorithms predictive accuracy for the 7 clinical outcome measures for each of aTSA and rTSA were quantified using the mean absolute error and the area under the receiver operating curve (AUROC). Results. Predict+ was released in November 2020 and is currently in limited launch in the US and select international markets. Predict+ utilizes an interactive graphical user interface to facilitate efficient entry of the preoperative inputs to generate personalized predictions of 7 clinical outcome measures achieved with aTSA and rTSA. Predict+ outputs a simple, patient-friendly graphical overview of preoperative status and a personalized 2-year outcome summary of aTSA and rTSA predictions for all 7 outcome measures to aid in the preoperative patient consultation process. Additionally, Predict+ outputs a detailed line-graph view of a patient's preoperative status and their personalized aTSA, rTSA, and aTSA vs. rTSA predicted outcomes for the 7 outcome measures at 6 postoperative timepoints. For each line-graph, the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) patient-satisfaction improvement thresholds are displayed to aid the surgeon in assessing improvement potential for aTSA and rTSA and also relative to an average age and gender matched patient. The initial clinical experience of Predict+ has been positive. Input of the preoperative patient data is efficient and generally completed in <5 minutes. However, continued workflow improvements are necessary to limit the occurrence of responder fatigue. The graphical user interface is intuitive and facilitated a rapid assessment of expected patient outcomes. We have not found the use of this tool to be disruptive of our clinic's workflow. Ultimately, this tool has positively shifted the preoperative consultation towards discussion of clinical outcomes data, and that has been helpful to guide a patient's understanding of what can be realistically achieved with shoulder arthroplasty. Discussion and Conclusions. Predict+ aims to improve a surgeon's ability to preoperatively counsel patients electing to undergo shoulder arthroplasty. We are hopeful this innovative tool will help align surgeon and patient expectations and ultimately improve patient satisfaction with this elective procedure. Future research is required, but our initial experience demonstrates the positive potential of this predictive tool

Aims

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

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

Purposes and Background. Musculoskeletal disorders including as back and neck pain are leading causes of work disability. Effective interventions exist (i.e. functional restoration, multidisciplinary biopsychosocial rehabilitation, workplace-based interventions, etc.), but it is difficult to select the optimal intervention for specific patients. The Work Assessment Triage Tool (WATT) is a clinical decision support tool developed using machine learning to help select interventions. The WATT algorithm categorizes patients based on individual, occupational, and clinical characteristics according to likelihood of successful return-to-work following rehabilitation. Internal validation showed acceptable classification accuracy, but WATT has not been tested beyond the original development sample. Our purpose was to externally validate the WATT. Methods and Results. A population-based cohort design was used, with administrative and clinical data extracted from a Canadian provincial compensation database. Data were available on workers being considered for rehabilitation between January 2013 and December 2016. Data was obtained on patient characteristics (ie. age, sex, education level), clinical factors (ie. diagnosis, part of body affected, pain and disability ratings), occupational factors (ie. occupation, employment status, modified work availability), type of rehabilitation program undertaken, and return-to-work outcomes (receipt of wage replacement benefits 30 days after assessment). Analysis included classification accuracy statistics of WATT recommendations for selecting interventions that lead to successful RTW outcomes. The sample included 5296 workers of which 33% had spinal conditions. Sensitivity of the WATT was 0.35 while specificity was 0.83. Overall accuracy was 73%. Conclusion. Accuracy of the WATT for selecting successful rehabilitation programs was modest. Algorithm revision and further validation is needed. No conflicts of interest. Sources of funding: Funding was provided by the Workers' Compensation Board of Alberta

Background. Metastatic bone patients who require surgery needs to be evaluated in order to maximise quality of life and avoiding functional impairment, minimising the risks connected to the surgical procedures. The best surgical procedure needs to be tailored on survival estimation. There are no current available tool or method to evaluate survival estimation with accuracy in patients with bone metastasis. We recently developed a clinical decision support tool, capable of estimating the likelihood of survival at 3 and 12 months following surgery for patients with operable skeletal metastases. After making it publicly available on . www.PATHFx.org. , we attempted to externally validate it using independent, international data. Methods. We collected data from patients treated at 13 Italian orthopaedic oncology referral centers between 2008 and 2012, then applied to PATHFx, which generated a probability of survival at three and 12-months for each patient. We assessed accuracy using the area under the receiver-operating characteristic curve (AUC), clinical utility using Decision Curve Analysis DCA), and compared the Italian patient data to the training set (United States) and first external validation set (Scandinavia). Results. The Italian dataset contained 287 records with at least 12 months follow-up information. The AUCs for the three-month and 12-month estimates was 0.80 and 0.77, respectively. There were missing data, including the surgeon's estimate of survival that was missing in the majority of records. Physiologically, Italian patients were similar to patients in the training and first validation sets. However notable differences were observed in the proportion of those surviving three and 12-months, suggesting differences in referral patterns and perhaps indications for surgery. Conclusions. PATHFx was successfully validated in an Italian dataset containing missing data. This study demonstrates its broad applicability to European patients, even in centers with differing treatment philosophies from those previously studied

Background. Metastatic bone patients who require surgery needs to be evaluated in order to maximise quality of life and avoiding functional impairment, minimising the risks connected to the surgical procedures. The best surgical procedure needs to be tailored on survival estimation. There are no current available tool or method to evaluate survival estimation with accuracy in patients with bone metastasis. We recently developed a clinical decision support tool, capable of estimating the likelihood of survival at 3 and 12 months following surgery for patients with operable skeletal metastases. After making it publicly available on . www.PATHFx.org. , we attempted to externally validate it using independent, international data. Methods. We collected data from patients treated at 13 Italian orthopaedic oncology referral centers between 2008 and 2012, then applied to PATHFx, which generated a probability of survival at three and 12-months for each patient. We assessed accuracy using the area under the receiver-operating characteristic curve (AUC), clinical utility using Decision Curve Analysis DCA), and compared the Italian patient data to the training set (United States) and first external validation set (Scandinavia). Results. The Italian dataset contained 287 records with at least 12 months follow-up information. The AUCs for the three-month and 12-month estimates was 0.80 and 0.77, respectively. There were missing data, including the surgeon's estimate of survival that was missing in the majority of records. Physiologically, Italian patients were similar to patients in the training and first validation sets. However notable differences were observed in the proportion of those surviving three and 12-months, suggesting differences in referral patterns and perhaps indications for surgery. Conclusions. PATHFx was successfully validated in an Italian dataset containing missing data. This study demonstrates its broad applicability to European patients, even in centers with differing treatment philosophies from those previously studied. Level of Evidence. IV. None of the authors have financial disclosures or conflicts of interest to declare. The study presented did not need the approval by ethics committee