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

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