The recently published Prophylactic Antibiotic Regimens In Tumor Surgery (PARITY) trial found no benefit in extending antibiotic prophylaxis from 24 hours to five days after endoprosthetic reconstruction for lower limb bone tumours. PARITY is the first randomized controlled trial in orthopaedic oncology and is a huge step forward in understanding antibiotic prophylaxis. However, significant gaps remain, including questions around antibiotic choice, particularly in the UK, where cephalosporins are avoided due to concerns of Cite this article:
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article:
Continuous technical improvement in spinal surgical procedures, with the aim of enhancing patient outcomes, can be assisted by the deployment of advanced technologies including navigation, intraoperative CT imaging, and surgical robots. The latest generation of robotic surgical systems allows the simultaneous application of a range of digital features that provide the surgeon with an improved view of the surgical field, often through a narrow portal. There is emerging evidence that procedure-related complications and intraoperative blood loss can be reduced if the new technologies are used by appropriately trained surgeons. Acceptance of the role of surgical robots has increased in recent years among a number of surgical specialities including general surgery, neurosurgery, and orthopaedic surgeons performing major joint arthroplasty. However, ethical challenges have emerged with the rollout of these innovations, such as ensuring surgeon competence in the use of surgical robotics and avoiding financial conflicts of interest. Therefore, it is essential that trainees aspiring to become spinal surgeons as well as established spinal specialists should develop the necessary skills to use robotic technology safely and effectively and understand the ethical framework within which the technology is introduced. Traditional and more recently developed platforms exist to aid skill acquisition and surgical training which are described. The aim of this narrative review is to describe the role of surgical robotics in spinal surgery, describe measures of proficiency, and present the range of training platforms that institutions can use to ensure they employ confident spine surgeons adequately prepared for the era of robotic spinal surgery. Cite this article:
The coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented challenges to healthcare systems worldwide. Orthopaedic departments have adopted business continuity models and guidelines for essential and non-essential surgeries to preserve hospital resources as well as protect patients and staff. These guidelines broadly encompass reduction of ambulatory care with a move towards telemedicine, redeployment of orthopaedic surgeons/residents to the frontline battle against COVID-19, continuation of education and research through web-based means, and cancellation of non-essential elective procedures. However, if containment of COVID-19 community spread is achieved, resumption of elective orthopaedic procedures and transition plans to return to normalcy must be considered for orthopaedic departments. The COVID-19 pandemic also presents a moral dilemma to the orthopaedic surgeon considering elective procedures. What is the best treatment for our patients and how does the fear of COVID-19 influence the risk-benefit discussion during a pandemic? Surgeons must deliberate the fine balance between elective surgery for a patient’s wellbeing versus risks to the operating team and utilization of precious hospital resources. Attrition of healthcare workers or Orthopaedic surgeons from restarting elective procedures prematurely or in an unsafe manner may render us ill-equipped to handle the second wave of infections. This highlights the need to develop effective screening protocols or preoperative COVID-19 testing before elective procedures in high-risk, elderly individuals with comorbidities. Alternatively, high-risk individuals should be postponed until the risk of nosocomial COVID-19 infection is minimal. In addition, given the higher mortality and perioperative morbidity of patients with COVID-19 undergoing surgery, the decision to operate must be carefully deliberated. As we ramp-up elective services and get “back to business” as orthopaedic surgeons, we have to be constantly mindful to proceed in a cautious and calibrated fashion, delivering the best care, while maintaining utmost vigilance to prevent the resurgence of COVID-19 during this critical transition period. Cite this article:
The COVID-19 virus is a tremendous burden for the Italian health system. The regionally-based Italian National Health System has been reorganized. Hospitals' biggest challenge was to create new intensive care unit (ICU) beds, as the existing system was insufficient to meet new demand, especially in the most affected areas. Our institution in the Milan metropolitan area of Lombardy, the epicentre of the infection, was selected as one of the three regional hub for major trauma, serving a population of more than three million people. The aims were the increase the ICU beds and the rationalization of human and structural resources available for treating COVID-19 patients. In our hub hospital, the reorganization aimed to reduce the risk of infection and to obtained resources, in terms of beds and healthcare personnel to be use in the COVID-19 emergency. Non-urgent outpatient orthopaedic activity and elective surgery was also suspended. A training programme for healthcare personnel started immediately. Orthopaedic and radiological pathways dedicated to COVID-19 patients, or with possible infection, have been established. In our orthopaedic department, we passed from 70 to 26 beds. Our goal is to treat trauma surgery's patient in the “golden 72 hours” in order to reduce the overall hospital length of stay. We applied an objective priority system to manage the flow of surgical procedures in the emergency room based on clinical outcomes and guidelines. Organizing the present to face the emergency is a challenge, but in the global plan of changes in hospital management one must also think about the near future. We reported the Milan metropolitan area orthopaedic surgery management during the COVID-19 pandemic. Our decisions are not based on scientific evidence; therefore, the decision on how reorganize hospitals will likely remain in the hands of individual countries.
In 2013, we introduced a specialized, centralized, and interdisciplinary team in our institution that applied a standardized diagnostic and treatment algorithm for the management of prosthetic joint infections (PJIs). The hypothesis for this study was that the outcome of treatment would be improved using this approach. In a retrospective analysis with a standard postoperative follow-up, 95 patients with a PJI of the hip and knee who were treated with a two-stage exchange between 2013 and 2017 formed the study group. A historical cohort of 86 patients treated between 2009 and 2011 not according to the standardized protocol served as a control group. The success of treatment was defined according to the Delphi criteria in a two-year follow-up.Aims
Patients and Methods
‘Big data’ is a term for data sets that are so
large or complex that traditional data processing applications are
inadequate. Billions of dollars have been spent on attempts to build predictive
tools from large sets of poorly controlled healthcare metadata.
Companies often sell reports at a physician or facility level based
on various flawed data sources, and comparative websites of ‘publicly
reported data’ purport to educate the public. Physicians should
be aware of concerns and pitfalls seen in such data definitions,
data clarity, data relevance, data sources and data cleaning when
evaluating analytic reports from metadata in health care. Cite this article:
The importance of accurate identification and reporting of surgical
site infection (SSI) is well recognised but poorly defined. Public
Health England (PHE) mandated collection of orthopaedic SSI data
in 2004. Data submission is required in one of four categories (hip
prosthesis, knee prosthesis, repair of neck of femur, reduction
of long bone fracture) for one quarter per year. Trusts are encouraged
to carry out post-discharge surveillance but this is not mandatory.
Recent papers in the orthopaedic literature have highlighted the
importance of SSI surveillance and the heterogeneity of surveillance
methods. However, details of current orthopaedic SSI surveillance
practice has not been described or quantified. All 147 NHS trusts in England were audited using a structured
questionnaire. Data was collected in the following categories: data
collection; data submission to PHE; definitions used; resource constraints;
post-discharge surveillance and SSI rates in the four PHE categories.
The response rate was 87.7%.Aims
Patients and Methods