In 2016, a hospital approved guideline on first line immunosuppression was introduced. This included evidence-based guidance on consent, prescription, dosing, safety and efficacy monitoring of corticos- teroid, azathioprine, methotrexate, mycophenolate, IVIg, SCIg, rituximab and cyclophosphamide use in common inflammatory neuromuscular diseases. It was accompanied by drug-specific patient information booklets and disease-specific clinical outcome measurement tools. The centre for neuromuscular diseases has not recorded any serious adverse events associated with these medications since its introduction.With junior doctors rotating in 3-6 monthly cycles, we identified the need for recurrent implementation efforts to maintain guideline impact. With an iterative process of four sequential cycles we:1) performed a survey of adequacy of admission/discharge documentation,2) created electronic patient record (EPR) proformas for admission/discharge,3) included a training session at changeover hospital induction,4) added a training session to mid-rotation peer-led teaching rota.This process resulted in optimal admission/discharge documentation (as per guidelines) of: Cycle 2) 61%/96%; Cycle 3) 100%/70%; Cycle 4) 86%/100%.Here we highlight the potential for loss of compliance with guideline use by junior medical staff over multiple changeovers, demonstrate the need for ongoing implementation efforts and underline the benefit of integrated electronic proformas (content in poster).
Ross syndrome is a rare syndrome of segmental autonomic dysfunction, characterised by a triad of tonic pupils, hyporeflexia and anhidrosis. We present an atypical case of such a patient, presenting with added features of cardiovascular autonomic dysfunction.Our case concerns a 66-year-old female, who experienced progressive stepwise deterioration in the functionality of her autonomic nervous system. Her symptoms began with a left Adie’s pupil, followed by a chronic dry cough and diarrhoea. The onset of anhidrosis and orthostatic hypotension subsequently occurred 30 and 40 years later, respectively.Autonomic function testing confirmed cardiovascular autonomic failure as well as deficiencies of sudomotor, salivary and tear function. Severe orthostatic hypotension was demonstrated along with an abnormal Valsalva response, and loss of the nocturnal circadian rhythm. Thermoregulatory and dynamic sweat testing further confirmed global anhidrosis.Our case highlights that patients do not always fit diagnostic criteria of eponymous syndromes, and usually demonstrate varying levels of autonomic dysfunction. We propose diagnosis of such atypical cases to be part of an Adie spectrum ganglionopathy, rather than attempting to fit into an eponymous syndrome phenotype. We further believe there to be an autoimmune basis to the Adie spectrum of disorders and have proposed immunotherapy as definitive management.a.basgaran@nhs.net|ABN Bursary
The human microbiome is a complex and dynamic community of microbes, thought to have symbiotic benefit to its host. Influences of the gut microbiome on brain microglia have been identified as a potential mechanism contributing to neurodegenerative diseases such as Alzheimer’s disease (AD), motor neurone disease and Parkinson’s disease. We hypothesise that machine learning applied to the population level gut microbiome will predict the prevalence of AD.Analyses were performed in R, using two large, open-access microbiome datasets (n=1006 & n >2000) (Lahti et al., 2014; Pasolli et al., 2017). Countries in these datasets were grouped based on AD prevalence and the microbiome profiles compared.In countries with a high prevalence of AD, there is a significantly lower diversity of the gut microbiome (p<0.001). A PERMANOVA (p<0.05) revealed significant differences between several taxa in countries with high vs low prevalence of AD. Additionally, using machine learning, we were able to predict the preva- lence of AD within a country based on the microbiome profile (Mean AUC 0.935 & 0.870).We conclude that differences in the microbiome can predict the varying prevalence of Alzheimer’s disease between countries. Our results support a key role of the gut microbiome in neurodegeneration at a population level.a.basgaran@nhs.net
580 Background: Despite improvements in breast cancer therapy, many patients do not respond to treatment: the response rate varies between 31 and 80% [Walks et al., 2019]. The tumor microenvironment contains a microbiome which is both cancer type-specific and different to surrounding normal tissue [Nejman et al., 2020]. It plays an important role in tumour microenvironment crosstalk with tumor cells and has been implicated in disease pathogenesis, being recognised in the latest hallmarks of cancer [Hanahan et Weinberg, 2011]. It has also successfully predicted treatment response [Chen et al., 2022; Hermida et al., 2022]. Here, we use conventional microbiome analysis alongside our proprietary mechanistically-powered technology platform to compare the intratumoral bacteria between responders and non-responders. We hypothesise that there are significant differences between those groups regarding composition and mechanistic function of the bacteria. Methods: The tumor microbiome can be deduced from existing sequencing data of tumor biopsy samples. Using 34 tumor DNA samples from 5 breast cancer studies, we compared the intratumoral bacterial profiles of responders to non-responders of various treatments. We performed microbiome analysis in R, including alpha diversity, compositional abundance profiles, and PERMANOVA (permutational analysis of variance). Additionally, BioCorteX CarbonMirror version dated 2023-02-14 was used to infer mechanistic links between bacterial species and the up- and downregulation of genes implicated in the hallmarks of cancer. Results: The results show significant differences in diversity and compositional profile between responders and non-responders: Non-responders have significantly higher alpha diversity ( p<0.05) and a higher proportion of Proteobacteria. PERMANOVA analysis revealed that while responders are more likely to have commensal bacteria, non-responder tumour microbiomes are more likely to harbor pathogenic species such as Mycoplasmopsis fermentans. Mechanistic analysis showed that for all responders the tumor microbiome is consistently promoting tumor suppressor genes and downregulating proto-oncogenes. In non-responders, however, the tumor microbiome is upregulating and downregulating both with unclear consistency. Conclusions: Our results show microbiome differences between responders and non-responders which were mechanistically implicated in tumor pathogenesis. Further analysis divided by the hallmarks of cancer is required to fully understand the non-responder microbiome links. The reported findings could make the tumor microbiome a useful biomarker aiding patient stratification for both prognosis and treatment choice. They also highlight a potentially meaningful mechanistic link between tumour microenvironment and treatment response that could be leveraged as a novel therapeutic avenue.
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