The aim of this study was to assess whether it is possible to predict the mortality, and the extent and time of neurological recovery from the time of the onset of symptoms and MRI grade, in patients with the cerebral fat embolism syndrome (CFES). This has not previously been investigated. The study included 34 patients who were diagnosed with CFES following trauma between 2012 and 2018. The clinical diagnosis was confirmed and the severity graded by MRI. We investigated the rate of mortality, the time and extent of neurological recovery, the time between the injury and the onset of symptoms, the clinical severity of the condition, and the MRI grade. All patients were male with a mean age of 29.7 years (18 to 70). The mean follow-up was 4.15 years (2 to 8), with neurological recovery being assessed by the Glasgow Outcome Scale and the Mini-Mental State Examination.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
