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Bone & Joint Open
Vol. 1, Issue 9 | Pages 556 - 561
14 Sep 2020
Clough TM Shah N Divecha H Talwalkar S

Aims. The exact risk to patients undergoing surgery who develop COVID-19 is not yet fully known. This study aims to provide the current data to allow adequate consent regarding the risks of post-surgery COVID-19 infection and subsequent COVID-19-related mortality. Methods. All orthopaedic trauma cases at the Wrightington Wigan and Leigh NHS Foundation Trust from ‘lockdown’ (23 March 2020) to date (15 June 2020) were collated and split into three groups. Adult ambulatory trauma surgeries (upper limb trauma, ankle fracture, tibial plateau fracture) and regional-specific referrals (periprosthetic hip fracture) were performed at a stand-alone elective site that accepted COVID-19-negative patients. Neck of femur fractures (NOFF) and all remaining non-NOFF (paediatric trauma, long bone injury) surgeries were performed at an acute site hospital (mixed green/blue site). Patients were swabbed for COVID-19 before surgery on both sites. Age, sex, nature of surgery, American Society of Anaesthesiologists (ASA) grade, associated comorbidity, length of stay, development of post-surgical COVID-19 infection, and post-surgical COVID-19-related deaths were collected. Results. At the elective site, 225 patients underwent orthopaedic trauma surgery; two became COVID-19-positive (0.9%) in the immediate perioperative period, neither of which was fatal. At the acute site, 93 patients underwent non-NOFF trauma surgery, of whom six became COVID-19-positive (6.5%) and three died. A further 84 patients underwent NOFF surgery, seven becoming COVID-19 positive (8.3%) and five died. Conclusion. At the elective site, the rate of COVID-19 infection following orthopaedic trauma surgery was low, at 0.9%. At the acute mixed site (typical district general hospital), for non-NOFF surgery there was a 6.5% incidence of post-surgical COVID-19 infection (seven-fold higher risk) with 50% COVID-19 mortality; for NOFF surgery, there was an 8.3% incidence of post-surgical COVID-19 infection, with 71% COVID-19 mortality. This is likely to have significance when planning a resumption of elective orthopaedic surgery and for consent to the patient. Cite this article: Bone Joint Open 2020;1-9:556–561


Bone & Joint Open
Vol. 4, Issue 8 | Pages 643 - 651
24 Aug 2023
Langit MB Tay KS Al-Omar HK Barlow G Bates J Chuo CB Muir R Sharma H

Aims

The standard of wide tumour-like resection for chronic osteomyelitis (COM) has been challenged recently by adequate debridement. This paper reviews the evolution of surgical debridement for long bone COM, and presents the outcome of adequate debridement in a tertiary bone infection unit.

Methods

We analyzed the retrospective record review from 2014 to 2020 of patients with long bone COM. All were managed by multidisciplinary infection team (MDT) protocol. Adequate debridement was employed for all cases, and no case of wide resection was included.


Bone & Joint Open
Vol. 1, Issue 6 | Pages 229 - 235
9 Jun 2020
Lazizi M Marusza CJ Sexton SA Middleton RG

Aims

Elective surgery has been severely curtailed as a result of the COVID-19 pandemic. There is little evidence to guide surgeons in assessing what processes should be put in place to restart elective surgery safely in a time of endemic COVID-19 in the community.

Methods

We used data from a stand-alone hospital admitting and operating on 91 trauma patients. All patients were screened on admission and 100% of patients have been followed-up after discharge to assess outcome.


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.