Recombinant tissue-type plasminogen activator versus a novel dosing regimen of urokinase in acute pulmonary embolism: a randomized controlled multicenter trial

AbstractThrombolysis of acute pulmonary embolism can be accomplished more rapidly and safely with 100 mg of recombinant human tissue-type plasminogen activator (rt-PA) (Activase) than with a conventional dose of urokinase (Abbokinase) given as a 4,400-U/kg bolus dose, followed by 4,400 U/kg per h for 24 h. To determine the effects of a more concentrated urokinase dose administered over a shorter time course, this trial enrolled 90 patients with baseline perfusion lung scans and angiographically documented pulmonary embolism. They were randomized to receive either 100 mg/2 h of rt-PA or a novel dosing regimen of urokinase: 3 million U/2 h with the initial 1 million U given as a bolus injection over 10 min. Both drugs were delivered through a peripheral vein.To assess efficacy after initiation of therapy, repeat pulmonary angiograms at 2 h were performed in 87 patients and then graded in a blinded manner by a panel of six investigators. Of the 42 patients allocated to rt-PA therapy, 79% showed angiographic improvement at 2 h, compared with 67% of the 45 patients randomized to urokinase therapy (95% confidence interval for the difference in these proportions [rt-PA minus urokinase] is −6.6% to 30.4%; p = 0.11). The mean change in perfusion lung scans between baseline and 24 h was similar for both treatments. Three patients (two treated with rt-PA and one with urokinase) had an intracranial hemorrhage, which was fatal in one.The results indicate that a 2-h regimen of rt-PA and a new dosing regimen of urokinase exhibit similar efficacy and safety for treatment of acute pulmonary embolism

Clinical effectiveness of septoplasty versus medical management for nasal airways obstruction:multicentre, open label, randomised controlled trial

OBJECTIVE: To assess the clinical effectiveness of septoplasty.DESIGN: Multicentre, randomised controlled trial.SETTING: 17 otolaryngology clinics in the UK's National Health Service.PARTICIPANTS: 378 adults (≥18 years, 67% men) newly referred with symptoms of nasal obstruction associated with septal deviation and at least moderate symptoms of nasal obstruction (score &gt;30 on the Nasal Obstruction and Symptom Evaluation (NOSE) scale).INTERVENTIONS: Participants were randomised 1:1 to receive either septoplasty (n=188) or defined medical management (n=190, nasal steroid and saline spray for six months), stratified by baseline symptom severity and sex.MAIN OUTCOME MEASURES: The primary outcome measure was patient reported score on the Sino-Nasal Outcome Test-22 (SNOT-22) at six months, with 9 points defined as the minimal clinically important difference. Secondary outcomes included quality of life and objective nasal airflow measures.RESULTS: Mean SNOT-22 scores at six months were 19.9 (95% confidence interval 17.0 to 22.7) in the septoplasty arm (n=152, intention-to-treat population) and 39.5 (36.1 to 42.9) in the medical management arm (n=155); an estimated 20.0 points lower (better) for participants randomised to receive septoplasty (95% confidence interval 16.4 to 23.6, P&lt;0.001, adjusted for baseline continuous SNOT-22 score and the stratification variables sex and baseline NOSE severity categories). Greater improvement in SNOT-22 scores was predicted by higher baseline symptom severity scores. Quality of life outcomes and nasal airflow measures (including peak nasal inspiratory flow and absolute inhalational nasal partitioning ratio) improved more in participants in the septoplasty group. Readmission to hospital with bleeding after septoplasty occurred in seven participants (4% of 174 who had septoplasty), and a further 20 participants (12%) required antibiotics for infections.CONCLUSIONS: Septoplasty is a more effective intervention than a defined medical management regimen with a nasal steroid and saline spray in adults with nasal obstruction associated with a deviated nasal septum.TRIAL REGISTRATION: ISRCTN Registry ISRCTN16168569.</p

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics. Here we examine a large cohort (the INTERVAL study; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

The Perioperative Quality Improvement Programme (PQIP patient study): protocol for a UK multicentre, prospective cohort study to measure quality of care and outcomes after major surgery

INTRODUCTION: Major surgery accounts for a substantial proportion of health service activity, due not only to the primary procedure, but the longer-term health implications of poor short-term outcome. Data from small studies or from outside the UK indicate that rates of complications and failure to rescue vary between hospitals, as does compliance with best practice processes. Within the UK, there is currently no system for monitoring postoperative complications (other than short-term mortality) in major non-cardiac surgery. Further, there is variation between national audit programmes, in the emphasis placed on quality assurance versus quality improvement, and therefore the principles of measurement and reporting which are used to design such programmes. METHODS AND ANALYSIS: The PQIP patient study is a multi-centre prospective cohort study which recruits patients undergoing major surgery. Patient provide informed consent and contribute baseline and outcome data from their perspective using a suite of patient-reported outcome tools. Research and clinical staff complete data on patient risk factors and outcomes in-hospital, including two measures of complications. Longer-term outcome data are collected through patient feedback and linkage to national administrative datasets (mortality and readmissions). As well as providing a uniquely granular dataset for research, PQIP provides feedback to participating sites on their compliance with evidence-based processes and their patients' outcomes, with the aim of supporting local quality improvement. ETHICS AND DISSEMINATION: Ethical approval has been granted by the Health Research Authority in the UK. Dissemination of interim findings (non-inferential) will form a part of the improvement methodology and will be provided to participating centres at regular intervals, including near-real time feedback of key process measures. Inferential analyses will be published in the peer-reviewed literature, supported by a comprehensive multi-modal communications strategy including to patients, policy makers and academic audiences as well as clinicians

Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci

Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but until recently, little was known about the genetic factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 white British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10(-8)), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness, 95% CI [1.15, 1.27], P = 3x10(-12)) and PER2 (1.09 odds of morningness, 95% CI [1.06, 1.12], P = 4x10(-10)). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,283 23andMe participants;thirteen of the chronotype signals remained associated at P<5x10(-8) on meta-analysis and eleven of these reached P< 0.05 in the same direction in the 23andMe study. We also replicated 9 additional variants identified when the 23andMe study was used as a discovery GWAS of chronotype (all P<0.05 and meta-analysis P<5x10(-8)). For sleep duration, we replicated one known signal in PAX8 (2.6 minutes per allele, 95% CI [1.9, 3.2], P = 5.7x10(-16)) and identified and replicated two novel associations at VRK2 (2.0 minutes per allele, 95% CI [1.3, 2.7], P = 1.2x10(-9);and 1.6 minutes per allele, 95% CI [1.1, 2.2], P = 7.6x10(-9)). Although we found genetic correlation between chronotype and BMI (rG = 0.056, P = 0.05);undersleeping and BMI (rG = 0.147, P = 1x10(-5)) and over-sleeping and BMI (rG = 0.097, P = 0.04), Mendelian Randomisation analyses, with limited power, provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study brings the total number of loci associated with chronotype to 22 and with sleep duration to three, and provides new insights into the biology of sleep and circadian rhythms in humans