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Immunomodulatory therapy in children with paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS, MIS-C; RECOVERY): a randomised, controlled, open-label, platform trial.
BackgroundPaediatric multisystem inflammatory syndrome temporally associated with SARS-CoV-2 (PIMS-TS), also known as multisystem inflammatory syndrome in children (MIS-C) emerged in April, 2020. The paediatric comparisons within the RECOVERY trial aimed to assess the effect of intravenous immunoglobulin or corticosteroids compared with usual care on duration of hospital stay for children with PIMS-TS and to compare tocilizumab (anti-IL-6 receptor monoclonal antibody) or anakinra (anti-IL-1 receptor antagonist) with usual care for those with inflammation refractory to initial treatment.MethodsWe did this randomised, controlled, open-label, platform trial in 51 hospitals in the UK. Eligible patients were younger than 18 years and had been admitted to hospital for PIMS-TS. In the first randomisation, patients were randomly assigned (1:1:1) to usual care (no additional treatments), usual care plus methylprednisolone (10mg/kg per day for 3 consecutive days), or usual care plus intravenous immunoglobulin (a single dose of 2 g/kg). If further anti-inflammatory treatment was considered necessary, children aged at least 1 year could be considered for a second randomisation, in which patients were randomly assigned (1:2:2) to usual care, intravenous tocilizumab (12 mg/kg in patients <30 kg; 8mg/kg in patients ≥30 kg, up to a maximum dose of 800 mg), or subcutaneous anakinra (2 mg/kg once per day in patients ≥10 kg). Randomisation was by use of a web-based simple (unstratified) randomisation with allocation concealment. The primary outcome was duration of hospital stay. Analysis was by intention to treat. For treatments assessed in each randomisation, a single Bayesian framework assuming uninformative priors for treatment was used to jointly assess the efficacy of each intervention compared with usual care. The trial was registered with ISRCTN (50189673) and ClinicalTrials.gov (NCT04381936).FindingsBetween May 18, 2020, and Jan 20, 2022, 237 children with PIMS-TS were enrolled and included in the intention-to-treat analysis. Of the 214 patients who entered the first randomisation, 73 were assigned to receive intravenous immunoglobulin, 61 methylprednisolone, and 80 usual care. Of the 70 children who entered the second randomisation (including 23 who did not enter the first randomisation), 28 were assigned to receive tocilizumab, 14 anakinra, and 28 usual care. Mean age was 9·5 years (SD 3·8) in the randomisation and 9·6 years (3·6) in the second randomisation. 118 (55%) of 214 patients in the first randomisation and 39 (56%) of 70 patients in the second randomisation were male. 130 (55%) of 237 patients were Black, Asian, or minority ethnic, and 105 (44%) were White. Mean duration of hospital stay was 7·4 days (SD 0·4) in children assigned to intravenous immunoglobulin and 7·6 days (0·4) in children assigned to usual care (difference -0·1 days, 95% credible interval [CrI] -1·3 to 1·0; posterior probability 59%). Mean duration of hospital stay was 6·9 days (SD 0·5) in children assigned to methylprednisolone (difference from usual care -0·7 days, 95% CrI -1·9 to 0·6; posterior probability 87%). Mean duration of hospital stay was 6·6 days (SD 0·7) in children assigned to second-line tocilizumab and 9·9 days (0·9) in children assigned to usual care (difference -3·3 days, 95% CrI -5·6 to -1·0; posterior probability >99%). Mean duration of hospital stay was 8·5 days (SD 1·2) in children assigned to anakinra (difference from usual care -1·4 days, 95% CrI -4·3 to 1·8; posterior probability 84%). Two persistent coronary artery aneurysms were reported among patients assigned to usual care in the first randomisation. There were few cardiac arrythmias, bleeding, or thrombotic events in any group. Two children died; neither was considered related to study treatment.InterpretationModerate evidence suggests that, compared with usual care, first-line intravenous methylprednisolone reduces duration of hospital stay for children with PIMS-TS. Good evidence suggests that second-line tocilizumab reduces duration of hospital stay for children with inflammation refractory to initial treatment. Neither intravenous immunoglobulin nor anakinra had any effect on duration of hospital stay compared with usual care.FundingMedical Research Council and National Institute of Health Research.
Empagliflozin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial.
BackgroundEmpagliflozin has been proposed as a treatment for COVID-19 on the basis of its anti-inflammatory, metabolic, and haemodynamic effects. The RECOVERY trial aimed to assess its safety and efficacy in patients admitted to hospital with COVID-19.MethodsIn the randomised, controlled, open-label RECOVERY trial, several possible treatments are compared with usual care in patients hospitalised with COVID-19. In this analysis, we assess eligible and consenting adults who were randomly allocated in a 1:1 ratio to either usual standard of care alone or usual standard of care plus oral empagliflozin 10 mg once daily for 28 days or until discharge (whichever came first) using web-based simple (unstratified) randomisation with allocation concealment. The primary outcome was 28-day mortality; secondary outcomes were duration of hospitalisation and (among participants not on invasive mechanical ventilation at baseline) the composite of invasive mechanical ventilation or death. On March 3, 2023 the independent data monitoring committee recommended that the investigators review the data and recruitment was consequently stopped on March 7, 2023. The ongoing RECOVERY trial is registered with ISRCTN (50189673) and ClinicalTrials.gov (NCT04381936).FindingsBetween July 28, 2021 and March 6, 2023, 4271 patients were randomly allocated to receive either empagliflozin (2113 patients) or usual care alone (2158 patients). Primary and secondary outcome data were known for greater than 99% of randomly assigned patients. Overall, 289 (14%) of 2113 patients allocated to empagliflozin and 307 (14%) of 2158 patients allocated to usual care died within 28 days (rate ratio 0·96 [95% CI 0·82-1·13]; p=0·64). There was no evidence of significant differences in duration of hospitalisation (median 8 days for both groups) or the proportion of patients discharged from hospital alive within 28 days (1678 [79%] in the empagliflozin group vs 1677 [78%] in the usual care group; rate ratio 1·03 [95% CI 0·96-1·10]; p=0·44). Among those not on invasive mechanical ventilation at baseline, there was no evidence of a significant difference in the proportion meeting the composite endpoint of invasive mechanical ventilation or death (338 [16%] of 2084 vs 371 [17%] of 2143; risk ratio 0·95 [95% CI 0·84-1·08]; p=0·44). Two serious adverse events believed to be related to empagliflozin were reported: both were ketosis without acidosis.InterpretationIn adults hospitalised with COVID-19, empagliflozin was not associated with reductions in 28-day mortality, duration of hospital stay, or risk of progressing to invasive mechanical ventilation or death so is not indicated for the treatment of such patients unless there is an established indication due to a different condition such as diabetes.FundingUK Research and Innovation (Medical Research Council) and National Institute of Health Research (MC_PC_19056), and Wellcome Trust (222406/Z/20/Z).TranslationsFor the Nepali, Hindi, Indonesian (Bahasa) and Vietnamese translations of the abstract see Supplementary Materials section.
Genome-wide characterization of circulating metabolic biomarkers.
Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.
Effectiveness of high-dose versus standard-dose quadrivalent influenza vaccine against recurrent hospitalizations and mortality in relation to influenza circulation: A post-hoc analysis of the DANFLU-1 randomized clinical trial.
ObjectivesTo evaluate the relative effectiveness of high-dose quadrivalent influenza vaccine (QIV-HD) versus standard-dose quadrivalent influenza vaccine (QIV-SD) against recurrent hospitalizations and its potential variation in relation to influenza circulation.MethodsWe did a post-hoc analysis of a pragmatic, open-label, randomized trial of QIV-HD versus QIV-SD performed during the 2021-2022 influenza season among adults aged 65-79 years. Participants were enrolled in October 2021-November, 2021 and followed for outcomes from 14 days postvaccination until 31 May, 2022. We investigated the following outcomes: Hospitalizations for pneumonia or influenza, respiratory hospitalizations, cardio-respiratory hospitalizations, cardiovascular hospitalizations, all-cause hospitalizations, and all-cause death. Outcomes were analysed as recurrent events. Cumulative numbers of events were assessed weekly. Cumulative relative effectiveness estimates were calculated and descriptively compared with influenza circulation. The trial is registered at Clinicaltrials.gov: NCT05048589.ResultsAmong 12,477 randomly assigned participants, receiving QIV-HD was associated with lower incidence rates of hospitalizations for pneumonia or influenza (10 vs. 33 events, incidence rate ratio [IRR] 0.30 [95% CI, 0.14-0.64]; p 0.002) and all-cause hospitalizations (647 vs. 742 events, IRR 0.87 [95% CI, 0.76-0.99]; p 0.032) compared with QIV-SD. Trends favouring QIV-HD were consistently observed over time including in the period before active influenza transmission; i.e. while the first week with a ≥10% influenza test positivity rate was calendar week 10, 2022, the first statistically significant reduction in hospitalizations for pneumonia or influenza was already observed by calendar week 3, 2022 (5 vs. 15 events, IRR 0.33 [95% CI, 0.11-0.94]; p 0.037).DiscussionIn a post-hoc analysis, QIV-HD was associated with lower incidence rates of hospitalizations for pneumonia or influenza and all-cause hospitalizations compared with QIV-SD, with trends evident independent of influenza circulation levels. Our exploratory results correspond to a number needed to treat of 65 (95% CI 35-840) persons vaccinated with QIV-HD compared with QIV-SD to prevent one additional all-cause hospitalization per season. Further research is needed to confirm these hypothesis-generating findings.
Observational and genetic associations between cardiorespiratory fitness and cancer: a UK Biobank and international consortia study.
BackgroundThe association of fitness with cancer risk is not clear.MethodsWe used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method.ResultsAfter a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O2⋅min-1⋅kg-1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73-0.89), colorectal (0.94, 0.90-0.99), and breast cancer (0.96, 0.92-0.99). In MR analyses, a 0.5 SD increase in genetically predicted O2⋅min-1⋅kg-1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86-0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated.DiscussionHigher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.
Machine learning approaches to the identification of children affected by prenatal alcohol exposure: A narrative review
Fetal alcohol spectrum disorders (FASDs) affect at least 0.8% of the population globally. The diagnosis of FASD is uniquely complex, with a heterogeneous physical and neurobehavioral presentation that requires multidisciplinary expertise for diagnosis. Many researchers have begun to incorporate machine learning approaches into FASD research to identify children who are affected by prenatal alcohol exposure, including those with FASD. This narrative review highlights these efforts. Following an introduction to machine learning, we summarize examples from the literature of neurobehavioral screening tools and physiologic markers of exposure. We discuss individual efforts, including models that classify FASD based on parent-reported neurocognitive or behavioral questionnaires, 3D facial imaging, brain imaging, DNA methylation patterns, microRNA profiles, cardiac orienting response, and dysmorphic facial features. We highlight model performance and discuss the limitations of these approaches. We conclude by considering the scalability of these approaches and how these machine learning models, largely developed from clinical samples or highly exposed birth cohorts, may perform in the general population.
Lightweight transformers for clinical natural language processing
Specialised pre-trained language models are becoming more frequent in Natural language Processing (NLP) since they can potentially outperform models trained on generic texts. BioBERT (Sanh et al.Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv: 1910.01108, 2019) and BioClinicalBERT (Alsentzer et al.Publicly available clinical bert embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72-78, 2019) are two examples of such models that have shown promise in medical NLP tasks. Many of these models are overparametrised and resource-intensive, but thanks to techniques like knowledge distillation, it is possible to create smaller versions that perform almost as well as their larger counterparts. In this work, we specifically focus on development of compact language models for processing clinical texts (i.e. progress notes, discharge summaries, etc). We developed a number of efficient lightweight clinical transformers using knowledge distillation and continual learning, with the number of parameters ranging from million to million. These models performed comparably to larger models such as BioBERT and ClinicalBioBERT and significantly outperformed other compact models trained on general or biomedical data. Our extensive evaluation was done across several standard datasets and covered a wide range of clinical text-mining tasks, including natural language inference, relation extraction, named entity recognition and sequence classification. To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks. The models and code used in this study can be found on our Huggingface profile at https://huggingface.co/nlpie and Github page at https://github.com/nlpie-research/Lightweight-Clinical-Transformers, respectively, promoting reproducibility of our results.
MRI economics: Balancing sample size and scan duration in brain wide association studies.
A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.
Martingale posterior distributions
The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we present a different perspective that focuses on missing observations as the source of statistical uncertainty, with the parameter of interest being known precisely given the entire population. We argue that the foundation of Bayesian inference is to assign a distribution on missing observations conditional on what has been observed. In the i.i.d. setting with an observed sample of size n, the Bayesian would thus assign a predictive distribution on the missing Yn+1:∞ conditional on Y1:n, which then induces a distribution on the parameter. We utilize Doob’s theorem, which relies on martingales, to show that choosing the Bayesian predictive distribution returns the conventional posterior as the distribution of the parameter. Taking this as our cue, we relax the predictive machine, avoiding the need for the predictive to be derived solely from the usual prior to posterior to predictive density formula. We introduce the martingale posterior distribution, which returns Bayesian uncertainty on any statistic via the direct specification of the joint predictive. To that end, we introduce new predictive methodologies for multivariate density estimation, regression and classification that build upon recent work on bivariate copulas.