Aims. Currently, there is no single, comprehensive national guideline for analgesic strategies for total joint replacement. We compared inpatient and outpatient opioid requirements following total hip arthroplasty (THA) versus
The COVID-19 pandemic drastically affected elective orthopaedic services globally as routine orthopaedic activity was largely halted to combat this global threat. Our institution (University College London Hospital, UK) previously showed that during the first peak, a large proportion of patients were hesitant to be listed for their elective lower limb procedure. The aim of this study is to assess if there is a patient perception change towards having elective surgery now that we have passed the peak of the second wave of the pandemic. This is a prospective study of 100 patients who were on the waiting list of a single surgeon for an elective hip or knee procedure. Baseline characteristics including age, American Society of Anesthesiologists (ASA) grade, COVID-19 risk, procedure type, and admission type were recorded. The primary outcome was patient consent to continue with their scheduled surgical procedure. Subgroup analysis was also conducted to define if any specific patient factors influenced decision to continue with surgeryAims
Methods
The safe resumption of elective orthopaedic surgery following the peak of the COVID-19 pandemic remains a significant challenge. A number of institutions have developed a COVID-free pathway for elective surgery patients in order to minimize the risk of viral transmission. The aim of this study is to identify the perioperative viral transmission rate in elective orthopaedic patients following the restart of elective surgery. This is a prospective study of 121 patients who underwent elective orthopaedic procedures through a COVID-free pathway. All patients underwent a 14-day period of self-isolation, had a negative COVID-19 test within 72 hours of surgery, and underwent surgery at a COVID-free site. Baseline patient characteristics were recorded including age, American Society of Anaesthesiologists (ASA) grade, body mass index (BMI), procedure, and admission type. Patients were contacted 14 days following discharge to determine if they had had a positive COVID-19 test (COVID-confirmed) or developed symptoms consistent with COVID-19 (COVID-19-presumed).Aims
Methods
As the peak of the COVID-19 pandemic passes, the challenge shifts to safe resumption of routine medical services, including elective orthopaedic surgery. Protocols including pre-operative self-isolation, COVID-19 testing, and surgery at a non-COVID-19 site have been developed to minimize risk of transmission. Despite this, it is likely that many patients will want to delay surgery for fear of contracting COVID-19. The aim of this study is to identify the number of patients who still want to proceed with planned elective orthopaedic surgery in this current environment. This is a prospective, single surgeon study of 102 patients who were on the waiting list for an elective hip or knee procedure during the COVID-19 pandemic. Baseline characteristics including age, ASA grade, COVID-19 risk, procedure type, surgical priority, and admission type were recorded. The primary outcome was patient consent to continue with planned surgical care after resumption of elective orthopaedic services. Subgroup analysis was also performed to determine if any specific patient factors influenced the decision to proceed with surgery.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