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Bone & Joint Open
Vol. 1, Issue 6 | Pages 236 - 244
11 Jun 2020
Verstraete MA Moore RE Roche M Conditt MA

Aims. 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. Methods. 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. Results. With an associated area under the receiver-operator curve ranging between 0.75 and 0.98, the optimized ML models resulted in good to excellent predictions. The best performing model used a random forest approach while considering both alignment and intra-articular load readings. Conclusion. The presented model has the potential to make experience available to surgeons adopting new technology, bringing expert opinion in their operating theatre, but also provides insight in the surgical decision process. More specifically, these promising outcomes indicated the relevance of considering the overall limb alignment in the coronal and sagittal plane to identify the appropriate surgical decision


Bone & Joint Open
Vol. 4, Issue 9 | Pages 696 - 703
11 Sep 2023
Ormond MJ Clement ND Harder BG Farrow L Glester A

Aims

The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.

Methods

Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.


Bone & Joint Open
Vol. 2, Issue 9 | Pages 745 - 751
7 Sep 2021
Yakkanti RR Sedani AB Baker LC Owens PW Dodds SD Aiyer AA

Aims

This study assesses patient barriers to successful telemedicine care in orthopaedic practices in a large academic practice in the COVID-19 era.

Methods

In all, 381 patients scheduled for telemedicine visits with three orthopaedic surgeons in a large academic practice from 1 April 2020 to 12 June 2020 were asked to participate in a telephone survey using a standardized Institutional Review Board-approved script. An unsuccessful telemedicine visit was defined as patient-reported difficulty of use or reported dissatisfaction with teleconferencing. Patient barriers were defined as explicitly reported barriers of unsatisfactory visit using a process-based satisfaction metric. Statistical analyses were conducted using analysis of variances (ANOVAs), ranked ANOVAs, post-hoc pairwise testing, and chi-squared independent analysis with 95% confidence interval.


Bone & Joint Open
Vol. 2, Issue 2 | Pages 134 - 140
24 Feb 2021
Logishetty K Edwards TC Subbiah Ponniah H Ahmed M Liddle AD Cobb J Clark C

Aims

Restarting planned surgery during the COVID-19 pandemic is a clinical and societal priority, but it is unknown whether it can be done safely and include high-risk or complex cases. We developed a Surgical Prioritization and Allocation Guide (SPAG). Here, we validate its effectiveness and safety in COVID-free sites.

Methods

A multidisciplinary surgical prioritization committee developed the SPAG, incorporating procedural urgency, shared decision-making, patient safety, and biopsychosocial factors; and applied it to 1,142 adult patients awaiting orthopaedic surgery. Patients were stratified into four priority groups and underwent surgery at three COVID-free sites, including one with access to a high dependency unit (HDU) or intensive care unit (ICU) and specialist resources. Safety was assessed by the number of patients requiring inpatient postoperative HDU/ICU admission, contracting COVID-19 within 14 days postoperatively, and mortality within 30 days postoperatively.


Bone & Joint Open
Vol. 1, Issue 10 | Pages 663 - 668
21 Oct 2020
Clement ND Oussedik S Raza KI Patton RFL Smith K Deehan DJ

Aims

The primary aim was to assess the rate of patient deferral of elective orthopaedic surgery and whether this changed with time during the coronavirus disease 2019 (COVID-19) pandemic. The secondary aim was to explore the reasons why patients wanted to defer surgery and what measures/circumstances would enable them to go forward with surgery.

Methods

Patients were randomly selected from elective orthopaedic waiting lists at three centres in the UK in April, June, August, and September 2020 and were contacted by telephone. Patients were asked whether they wanted to proceed or defer surgery. Patients who wished to defer were asked seven questions relating to potential barriers to proceeding with surgery and were asked whether there were measures/circumstances that would allow them to go forward with surgery.


Bone & Joint 360
Vol. 6, Issue 1 | Pages 41 - 42
1 Feb 2017
Dale-Skinner J


Bone & Joint 360
Vol. 5, Issue 1 | Pages 37 - 40
1 Feb 2016
Ribbans W