Now that we are in the deceleration phase of the COVID-19 pandemic, the focus has shifted to how to safely reinstate elective operating. Regional and speciality specific data is important to guide this decision-making process. This study aimed to review 30-day mortality for all patients undergoing orthopaedic surgery during the peak of the pandemic within our region. This multicentre study reviewed data on all patients undergoing trauma and orthopaedic surgery in a region from 18 March 2020 to 27 April 2020. Information was collated from regional databases. Patients were COVID-19-positive if they had positive laboratory testing and/or imaging consistent with the infection. 30-day mortality was assessed for all patients. Secondly, 30-day mortality in fracture neck of femur patients was compared to the same time period in 2019.Aims
Methods
The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments. Based on a clinical series of 479 primary total knees and 1,305 associated surgical decisions, various ML models were developed. These models identified the indicated surgical decision based on available, intra-operative alignment, and tibiofemoral load data.Aims
Methods
