We compared early post-operative rates of wound
infection in HIV-positive and -negative patients presenting with open
tibial fractures managed with surgical fixation. The wounds of 84 patients (85 fractures), 28 of whom were HIV
positive and 56 were HIV negative, were assessed for signs of infection
using the ASEPIS wound score. There were 19 women and 65 men with
a mean age of 34.8 years. A total of 57 fractures (17 HIV-positive The study does not support the hypothesis that HIV significantly
increases the rate of early wound or pin-site infection in open
tibial fractures. We would therefore suggest that a patient’s HIV
status should not alter the management of open tibial fractures
in patients who have a CD4 count >
350 cells/μl. Cite this article:
The Open-Fracture Patient Evaluation Nationwide (OPEN) study was performed to provide clarity in open fracture management previously skewed by small, specialist centre studies and large, unfocused registry investigations. We report the current management metrics of open fractures across the UK. Patients admitted to hospital with an open fracture (excluding phalanges or isolated hand injuries) between 1 June 2021 and 30 September 2021 were included. Institutional information governance approval was obtained at the lead site and all data entered using Research Electronic Data Capture software. All domains of the British Orthopaedic Association Standard for Open Fracture Management were recorded.Aims
Method
This study estimated trends in incidence of open fractures and the adherence to clinical standards for open fracture care in England. Longitudinal data collected by the Trauma Audit and Research Network were used to identify 38,347 patients with open fractures, and a subgroup of 12,170 with severe open fractures of the tibia, between 2008 and 2019 in England. Incidence rates per 100,000 person-years and 95% confidence intervals were calculated. Clinical care was compared with the British Orthopaedic Association Standards for Trauma and National Major Trauma Centre audit standards.Aims
Methods
The aim of this study was to investigate the relationship between the Orthopaedic Trauma Society (OTS) classification of open fractures and economic costs. Resource use was measured during the six months that followed open fractures of the lower limb in 748 adults recruited as part of two large clinical trials within the UK Major Trauma Research Network. Resource inputs were valued using unit costs drawn from primary and secondary sources. Economic costs (GBP sterling, 2017 to 2018 prices), estimated from both a NHS and Personal Social Services (PSS) perspective, were related to the degree of complexity of the open fracture based on the OTS classification.Aims
Methods
Open fractures of the tibia are a heterogeneous group of injuries that can present a number of challenges to the treating surgeon. Consequently, few surgeons can reliably advise patients and relatives about the expected outcomes. The aim of this study was to determine whether these outcomes are predictable by using the Ganga Hospital Score (GHS). This has been shown to be a useful method of scoring open injuries to inform wound management and decide between limb salvage and amputation. We collected data on 182 consecutive patients with a type II, IIIA, or IIIB open fracture of the tibia who presented to our hospital between July and December 2016. For the purposes of the study, the patients were jointly treated by experienced consultant orthopaedic and plastic surgeons who determined the type of treatment. Separately, the study team (SP, HS, AD, JD) independently calculated the GHS and prospectively collected data on six outcomes for each patient. These included time to bony union, number of admissions, length of hospital stay, total length of treatment, final functional score, and number of operations. Spearman’s correlation was used to compare GHS with each outcome. Forward stepwise linear regression was used to generate predictive models based on components of the GHS. Five-fold cross-validation was used to prevent models from over-fitting.Aims
Methods
