Perioperative consultative hematology: can you clear my patient for a procedure?

Periprocedural management of antithrombotics is a common but challenging clinical scenario that renders patients vulnerable to potential adverse events such as bleeding and thrombosis. Over the past decade, periprocedural antithrombotic approaches have changed considerably with the advent of direct oral anticoagulants (DOACs), as well as a paradigm shift away from bridging in many warfarin patients. Successfully navigating this high-risk period relies on a number of individualized patient assessments conducted within a framework of standardized, systematic approaches. It also requires a thorough understanding of antithrombotic pharmacokinetics, multidisciplinary coordination of care, and comprehensive patient education and empowerment. In this article, we provide clinicians with a practical, stepwise approach to periprocedural management of antithrombotic agents through case-based examples of relevant clinical scenarios

Genome modeling system: A knowledge management platform for genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

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

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

Somatic variation processing profile and workflow.

<p>To illustrate key GMS concepts, the processing profiles and workflow for the somatic variation pipeline are shown. Abbreviations: copy number variant (CNV), copy number amplification (CNA), genome analysis tool kit (GATK), insertion/deletion (Indel), loss of heterozygosity (LOH), mapping quality (MQ), single nucleotide variant (SNV), structural variant (SV), variant allele frequency (VAF).</p

Major GMS pipelines.

<p>A brief description of each analysis pipeline tested for initial release of the GMS.</p

Key concepts of the GMS.

<p>The genome modeling system is architected around the idea of a ‘genome model’. The following vignettes illustrate key concepts integral to these models: (<b>A</b>) A subject can be modeled multiple times, possibly each with distinct ‘processing profiles’. For example, two different models can be defined for the HCC1395 genome using the ‘reference alignment’ pipeline. In Model 1, the processing profile specifies the use of BWA for alignment and Samtools for variant detection. In Model 2, Bowtie2 and GATK are used for these steps instead. (<b>B</b>) A given processing profile can be used across a group of models, ensuring, for instance, that all subjects in a cohort are processed in similar ways. In this example, two different cell line genomes (HCC1395 and XY2123) have models defined of the exact same type, using the processing profile with BWA/Samtools specified. (<b>C</b>) A model has no results until a build is generated. If the model is updated to have new inputs, a new build is required. Builds are immutable snapshots of modeling pipeline results. In this example, the HCC1395 genome has a reference alignment model again making use of the BWA/Samtools profile. However, as new instrument data becomes available, new builds are constructed to reflect the most complete data. (<b>D</b>) When models are used as inputs for other models, the last complete build for the input model is used as an input for the downstream build. In this example, both tumor and normal genomes are available for an individual (in this case HCC1395). Reference alignment models are built for each sample and then both are used as inputs for a third ‘somatic variation’ model. In reality, it is the underlying data in the reference alignment builds that are used to create a somatic variation build, identifying all variants that are thought to be tumor specific.</p

HCC1395 (“TST1”) example input, models, and outputs.

<p>A test dataset for the HCC1395 cell line is provided with the GMS software to allow testing of software installation, and facilitate further development. It is also used to illustrate much of the current functionality of the GMS. HCC1395 tumor and the corresponding HCC1395BL ‘normal’ cell line DNA and RNA samples were sequenced by whole genome, exome, and RNA-seq methods producing six sets of instrument data for input to various GMS pipelines. Additional required inputs for the pipelines include a reference genome (e.g., GRCh37), gene annotations (e.g., Ensembl 67_37l), and variant databases (e.g., dbSNP37). Different versions (processing profiles) of the reference alignment were used to align WGS and exome DNA reads to the reference genome. A separate RNA-seq pipeline similarly aligns RNA reads. Alternate versions of the somatic variation pipeline are used to call various types of variants from exome and WGS data by comparing tumor and normal reference alignments. A differential expression pipeline identifies significantly altered transcript expression levels by comparing the tumor and normal RNA-seq alignments. Finally, the MedSeq pipeline summarizes all upstream pipelines into a single convenient result set. This includes a multitude of reports and visualizations for single nucleotide variants (SNVs), Indels (insertions and deletions), SVs (structural variants), CNVs (copy number variations), transcript fusions, differentially expressed genes, alternatively expressed isoforms, and much more. Data types are further integrated to, for example, identify which variants at the DNA level are expressed at the RNA level and which events affect known cancer driver genes or druggable targets.</p

Circos plot of HCC1395 tumor/normal comparison.

<p>Circos is a popular tool for summarizing genomic events in a tumor genome. This is just one of many automatically generated visualizations made possible by the GMS. In this example, the WGS, exome and RNA-seq data for HCC1395 are displayed in several tracks along with additional visualizations illustrating individual events. Moving inwards, SNVs and Indels are plotted on the outermost track, then highly expressed genes, CNVs, and finally chromosomal translocations at the center. For events predicted to affect protein coding genes, additional plots are auto-generated to display the mutation position relative to protein domains and previously reported mutations from the Cosmic database, as illustrated in the topmost plot. Moving clockwise, a screenshot of IGV demonstrates one of the somatic deletions identified. IGV XML sessions are automatically generated to allow rapid manual review of all predicted events. Next, a histogram illustrates the expression of a single highly expressed gene relative to the distribution of expression for all genes. Then, a CNV plot is shown for an amplified portion of one chromosome. Finally, the coverage and supporting reads for a chromosomal translocation are depicted.</p