There is little published on the outcomes after restarting elective orthopaedic procedures following cessation of surgery due to the COVID-19 pandemic. During the pandemic, the reported perioperative mortality in patients who acquired SARS-CoV-2 infection while undergoing elective orthopaedic surgery was 18% to 20%. The aim of this study is to report the surgical outcomes, complications, and risk of developing COVID-19 in 2,316 consecutive patients who underwent elective orthopaedic surgery in the latter part of 2020 and comparing it to the same, pre-pandemic, period in 2019. A retrospective service evaluation of patients who underwent elective surgical procedures between 16 June 2020 and 12 December 2020 was undertaken. The number and type of cases, demographic details, American society of Anesthesiologists (ASA) grade, BMI, 30-day readmission rates, mortality, and complications at one- and six-week intervals were obtained and compared with patients who underwent surgery during the same six-month 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
