Shoulder replacement surgery is a well-established orthopaedic procedure designed to significantly enhance patients’ quality of life. However, the prevailing preoperative admission practices within our tertiary shoulder surgery unit involve a two-stage group and save testing process, necessitating an admission on the evening before surgery. This protocol may unnecessarily prolong hospital stays without yielding substantial clinical benefits. The principal aim of our study is to assess the necessity of conducting two preoperative group and save blood tests and to evaluate the requirement for blood transfusions in shoulder arthroplasty surgeries. A secondary objective is to reduce hospital stay durations and the associated admission costs for patients undergoing shoulder arthroplasty. We conducted a retrospective observational study covering the period from 1st January 2023 to 31st August 2023, collecting data from shoulder arthroplasty procedures across three hospitals within the Aneurin Bevan University Health Board. Our analysis included 21 total shoulder replacement cases and 13 reverse shoulder replacement cases. Notably, none of the patients required postoperative blood transfusions. The mean haemoglobin drop observed was 14 g/L for total shoulder replacements and 15 g/L for reverse shoulder replacements. The mean elective admission duration was 2.4 nights for total shoulder replacements and 2 nights for reverse shoulder replacements. Our data indicated that hospital stays were extended by one night primarily due to the preoperative group and save blood tests. In light of these findings, we propose a more streamlined admission process for elective shoulder replacement surgery, eliminating the need for the evening-before-surgery group and save testing. Hospital admissions in these units incur a cost of approximately £500 per night, while the group and save blood tests cost around £30 each. This revised admission procedure is expected to optimise the use of healthcare resources and improve patient satisfaction without compromising clinical care

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

INTRODUCTION. Allograft reconstruction after resection of primary bone sarcomas has a non-union rate of approximately 20%. Achieving a wide surface area of contact between host and allograft bone is one of the most important factors to help reduce the non-union rate. We developed a novel technique of haptic robot-assisted surgery to reconstruct bone defects left after primary bone sarcoma resection with structural allograft. METHODS. Using a sawbone distal femur joint-sparing hemimetaphyseal resection/reconstruction model, an identical bone defect was created in six sawbone distal femur specimens. A tumor-fellowship trained orthopedic surgeon reconstructed the defect using a simulated sawbone allograft femur. First, a standard, ‘all-manual’ technique was used to cut and prepare the allograft to best fit the defect. Then, using an identical sawbone copy of the allograft, the novel haptic-robot technique was used to prepare the allograft to best fit the defect. All specimens were scanned via CT. Using a separately validated technique, the surface area of contact between host and allograft was measured for both (1) the all-manual reconstruction and (2) the robot-assisted reconstruction. All contact surface areas were normalized by dividing absolute contact area by the available surface area on the exposed cut surface of host bone. RESULTS. The mean area of contact between host and allograft bone was 24% (of the available host surface area) for the all-manual group and 76% for the haptic robot-assisted group (p=0.004). CONCLUSIONS. This is the first report to our knowledge of using haptic robot technology to assist in structural bone allograft reconstruction of defects left after primary bone tumor resection. The findings strongly indicate that this technology has the potential to be of substantial clinical benefit. Further studies are warranted