Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Comparison of subsequent injury categorisation (SIC) models and their application in a sporting population

Background: The original subsequent injury categorisation (SIC-1.0) model aimed to classify relationships between chronological injury sequences to provide insight into the complexity and causation of subsequent injury occurrence. An updated model has recently been published. Comparison of the data coded according to the original and revised subsequent injury categorisation (SIC-1.0 and SIC-2.0) models has yet been formally compared. Methods: Medical attention injury data was prospectively collected for 42 elite water polo players over an 8 month surveillance period. The SIC-1.0 and SIC-2.0 models were retrospectively applied to the injury data. The injury categorisation from the two models was compared using descriptive statistics. Results: Seventy-four injuries were sustained by the 42 players (median = 2, range = 0-5), of which 32 injuries (43.2%) occurred subsequent to a previous injury. The majority of subsequent injuries were coded as occurring at a different site and being of a different nature, while also being considered clinically unrelated to the previous injury (SIC-1.0 category 10 = 57.9%; SIC-2.0 clinical category 16 = 54.4%). Application of the SIC-2.0 model resulted in a greater distribution of category allocation compared to the SIC-1.0 model that reflects a greater precision in the SIC-2.0 model. Conclusions: Subsequent injury categorisation of sport injury data can be undertaken using either the original (SIC-1.0) or the revised (SIC-2.0) model to obtain similar results. However, the SIC-2.0 model offers the ability to identify a larger number of mutually exclusive categories, while not relying on clinical adjudication for category allocation. The increased precision of SIC-2.0 is advantageous for clinical application and consideration of injury relationships

Genetic variants of methionine metabolism and X-ALD phenotype generation: results of a new study sample

X-linked adrenoleukodystrophy (X-ALD) is the most common inherited leukodystrophy. Nevertheless, no genotype-phenotype correlation has been established so far. Unidentified modifier genes or other cofactors are suspected to modulate phenotype and prognosis. We recently described polymorphisms of methionine metabolism as possible disease modifiers in X-ALD. To retest these findings, we analyzed 172 new DNA samples of X-ALD patients from different populations (France, Germany, USA, China) by genotyping eight genetic variants of methionine metabolism, including DHFR c.594+59del19bp, CBS c.844_855ins68, MTR c.2756A>G, MTHFR c.677C>T and c.1298A>C, MTRR c.60A>G, RFC1 c.80G>A, and Tc2 c.776C>G. We compared three X-ALD phenotypes: childhood-onset cerebral demyelinating inflammatory type (CCALD; n = 82), adulthood onset with focal cerebral demyelination (ACALD; n = 38), and adulthood onset without cerebral demyelination (AMN; n = 52). The association of genotypes and phenotypes was analyzed with univariate two-sided Pearson's chi(2). In the comparison between AMN and CCALD, the G allele of Tc2 c.776C>G was associated with X-ALD phenotypes (chi(2) = 6.1; P = 0.048). The prevalence of the GG genotype of Tc2 c.776C>G was higher in patients with CNS demyelination compared to those without CNS demyelination (chi(2) = 4.42; P = 0.036). The GG genotype was also more frequent in CCALD compared to AMN (chi(2) = 4.7; P = 0.031). The other polymorphisms did not show any significant associations in this study sample. Whereas the influence of other polymorphisms of methionine metabolism was not confirmed, the present study supports the previously made observation that the Tc2 genotype contributes to X-ALD phenotype generation