The development of spinal deformity in children with underlying neurodisability can affect their ability to function and impact on their quality of life, as well as compromise provision of nursing care. Patients with neuromuscular spinal deformity are among the most challenging due to the number and complexity of medical comorbidities that increase the risk for severe intraoperative or postoperative complications. A multidisciplinary approach is mandatory at every stage to ensure that all nonoperative measures have been applied, and that the treatment goals have been clearly defined and agreed with the family. This will involve input from multiple specialities, including allied healthcare professionals, such as physiotherapists and wheelchair services. Surgery should be considered when there is significant impact on the patients’ quality of life, which is usually due to poor sitting balance, back or costo-pelvic pain, respiratory complications, or problems with self-care and feeding. Meticulous preoperative assessment is required, along with careful consideration of the nature of the deformity and the problems that it is causing. Surgery can achieve good curve correction and results in high levels of satisfaction from the patients and their caregivers. Modern modular posterior instrumentation systems allow an effective deformity correction. However, the risks of surgery remain high, and involvement of the family at all stages of decision-making is required in order to balance the risks and anticipated gains of the procedure, and to select those patients who can mostly benefit from spinal correction.
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:
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:
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 United States and Canada are in the midst
of an epidemic of the use, misuse and overdose of opioids, and deaths
related to overdose. This is the direct result of overstatement
of the benefits and understatement of the risks of using opioids
by advocates and pharmaceutical companies. Massive amounts of prescription
opioids entered the community and were often diverted and misused.
Most other parts of the world achieve comparable pain relief using
fewer opioids. The misconceptions about opioids that created this epidemic are
finding their way around the world. There is particular evidence
of the increased prescription of strong opioids in Europe. Opioids are addictive and dangerous. Evidence is mounting that
the best pain relief is obtained through resilience. Opioids are
often prescribed when treatments to increase resilience would be
more effective. 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
Primary total knee arthroplasty (TKA) is a reliable
procedure with reproducible long-term results. Nevertheless, there
are conditions related to the type of patient or local conditions
of the knee that can make it a difficult procedure. The most common
scenarios that make it difficult are discussed in this review. These
include patients with many previous operations and incisions, and
those with severe coronal deformities, genu recurvatum, a stiff knee,
extra-articular deformities and those who have previously undergone
osteotomy around the knee and those with chronic dislocation of
the patella. Each condition is analysed according to the characteristics of
the patient, the pre-operative planning and the reported outcomes. When approaching the difficult primary TKA surgeons should use
a systematic approach, which begins with the review of the existing
literature for each specific clinical situation. Cite this article:
The patient with a painful arthritic knee awaiting
total knee arthroplasty (TKA) requires a multidisciplinary approach.
Optimal control of acute post-operative pain and the prevention
of chronic persistent pain remains a challenge. The aim of this
paper is to evaluate whether stratification of patients can help
identify those who are at particular risk for severe acute or chronic
pain. . Intense acute post-operative pain, which is itself a risk factor
for chronic pain, is more common in younger, obese female patients
and those suffering from central pain sensitisation. Pre-operative
pain, in the knee or elsewhere in the body, predisposes to central
sensitisation. Pain due to osteoarthritis of the knee may also trigger
neuropathic pain and may be associated with chronic medication like
opioids, leading to a state of nociceptive sensitisation called
‘opioid-induced hyperalgesia’. Finally, genetic and personality
related risk factors may also put patients at a higher risk for
the development of chronic pain. . Those identified as at risk for chronic pain would benefit from
specific peri-operative management including reduction in opioid
intake pre-operatively, the peri-operative use of antihyperalgesic
drugs such as ketamine and gabapentinoids, and a close post-operative
follow-up in a dedicated chronic pain