155 research outputs found

    Outcomes Associated With Oral Anticoagulants Plus Antiplatelets in Patients With Newly Diagnosed Atrial Fibrillation.

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    Importance: Patients with nonvalvular atrial fibrillation at risk of stroke should receive oral anticoagulants (OAC). However, approximately 1 in 8 patients in the Global Anticoagulant Registry in the Field (GARFIELD-AF) registry are treated with antiplatelet (AP) drugs in addition to OAC, with or without documented vascular disease or other indications for AP therapy. Objective: To investigate baseline characteristics and outcomes of patients who were prescribed OAC plus AP therapy vs OAC alone. Design, Setting, and Participants: Prospective cohort study of the GARFIELD-AF registry, an international, multicenter, observational study of adults aged 18 years and older with recently diagnosed nonvalvular atrial fibrillation and at least 1 risk factor for stroke enrolled between March 2010 and August 2016. Data were extracted for analysis in October 2017 and analyzed from April 2018 to June 2019. Exposure: Participants received either OAC plus AP or OAC alone. Main Outcomes and Measures: Clinical outcomes were measured over 3 and 12 months. Outcomes were adjusted for 40 covariates, including baseline conditions and medications. Results: A total of 24 436 patients (13 438 [55.0%] male; median [interquartile range] age, 71 [64-78] years) were analyzed. Among eligible patients, those receiving OAC plus AP therapy had a greater prevalence of cardiovascular indications for AP, including acute coronary syndromes (22.0% vs 4.3%), coronary artery disease (39.1% vs 9.8%), and carotid occlusive disease (4.8% vs 2.0%). Over 1 year, patients treated with OAC plus AP had significantly higher incidence rates of stroke (adjusted hazard ratio [aHR], 1.49; 95% CI, 1.01-2.20) and any bleeding event (aHR, 1.41; 95% CI, 1.17-1.70) than those treated with OAC alone. These patients did not show evidence of reduced all-cause mortality (aHR, 1.22; 95% CI, 0.98-1.51). Risk of acute coronary syndrome was not reduced in patients taking OAC plus AP compared with OAC alone (aHR, 1.16; 95% CI, 0.70-1.94). Patients treated with OAC plus AP also had higher rates of all clinical outcomes than those treated with OAC alone over the short term (3 months). Conclusions and Relevance: This study challenges the practice of coprescribing OAC plus AP unless there is a clear indication for adding AP to OAC therapy in newly diagnosed atrial fibrillation

    New AI Prediction Model Using Serial PT-INR Measurements in AF Patients on VKAs: GARFIELD-AF

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    Aims: Most clinical risk stratification models are based on measurement at a single time-point rather than serial measurements. Artificial intelligence (AI) is able to predict one-dimensional outcomes from multi-dimensional datasets. Using data from Global Anticoagulant Registry in the Field (GARFIELD)-AF registry, a new AI model was developed for predicting clinical outcomes in atrial fibrillation (AF) patients up to 1 year based on sequential measures of prothrombin time international normalized ratio (PT-INR) within 30 days of enrolment. Methods and results: Patients with newly diagnosed AF who were treated with vitamin K antagonists (VKAs) and had at least three measurements of PT-INR taken over the first 30 days after prescription were analysed. The AI model was constructed with multilayer neural network including long short-term memory and one-dimensional convolution layers. The neural network was trained using PT-INR measurements within days 0–30 after starting treatment and clinical outcomes over days 31–365 in a derivation cohort (cohorts 1–3; n = 3185). Accuracy of the AI model at predicting major bleed, stroke/systemic embolism (SE), and death was assessed in a validation cohort (cohorts 4–5; n = 1523). The model’s c-statistic for predicting major bleed, stroke/SE, and all-cause death was 0.75, 0.70, and 0.61, respectively. Conclusions: Using serial PT-INR values collected within 1 month after starting VKA, the new AI model performed better than time in therapeutic range at predicting clinical outcomes occurring up to 12 months thereafter. Serial PT-INR values contain important information that can be analysed by computer to help predict adverse clinical outcomes

    A survey of integral α-helical membrane proteins

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    Membrane proteins serve as cellular gatekeepers, regulators, and sensors. Prior studies have explored the functional breadth and evolution of proteins and families of particular interest, such as the diversity of transport-associated membrane protein families in prokaryotes and eukaryotes, the composition of integral membrane proteins, and family classification of all human G-protein coupled receptors. However, a comprehensive analysis of the content and evolutionary associations between membrane proteins and families in a diverse set of genomes is lacking. Here, a membrane protein annotation pipeline was developed to define the integral membrane genome and associations between 21,379 proteins from 34 genomes; most, but not all of these proteins belong to 598 defined families. The pipeline was used to provide target input for a structural genomics project that successfully cloned, expressed, and purified 61 of our first 96 selected targets in yeast. Furthermore, the methodology was applied (1) to explore the evolutionary history of the substrate-binding transmembrane domains of the human ABC transporter superfamily, (2) to identify the multidrug resistance-associated membrane proteins in whole genomes, and (3) to identify putative new membrane protein families

    Cardioversion in patients with newly diagnosed non-valvular atrial fibrillation: observational study using prospectively collected registry data.

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    OBJECTIVE: To investigate the clinical outcomes of patients who underwent cardioversion compared with those who did not have cardioverson in a large dataset of patients with recent onset non-valvular atrial fibrillation. DESIGN: Observational study using prospectively collected registry data (Global Anticoagulant Registry in the FIELD-AF-GARFIELD-AF). SETTING: 1317 participating sites in 35 countries. PARTICIPANTS: 52 057 patients aged 18 years and older with newly diagnosed atrial fibrillation (up to six weeks' duration) and at least one investigator determined stroke risk factor. MAIN OUTCOME MEASURES: Comparisons were made between patients who received cardioversion and those who had no cardioversion at baseline, and between patients who received direct current cardioversion and those who had pharmacological cardioversion. Overlap propensity weighting with Cox proportional hazards models was used to evaluate the effect of cardioversion on clinical endpoints (all cause mortality, non-haemorrhagic stroke or systemic embolism, and major bleeding), adjusting for baseline risk and patient selection. RESULTS: 44 201 patients were included in the analysis comparing cardioversion and no cardioversion, and of these, 6595 (14.9%) underwent cardioversion at baseline. The propensity score weighted hazard ratio for all cause mortality in the cardioversion group was 0.74 (95% confidence interval 0.63 to 0.86) from baseline to one year follow-up and 0.77 (0.64 to 0.93) from one year to two year follow-up. Of the 6595 patients who had cardioversion at baseline, 299 had a follow-up cardioversion more than 48 days after enrolment. 7175 patients were assessed in the analysis comparing type of cardioversion: 2427 (33.8%) received pharmacological cardioversion and 4748 (66.2%) had direct current cardioversion. During one year follow-up, event rates (per 100 patient years) for all cause mortality in patients who received direct current and pharmacological cardioversion were 1.36 (1.13 to 1.64) and 1.70 (1.35 to 2.14), respectively. CONCLUSION: In this large dataset of patients with recent onset non-valvular atrial fibrillation, a small proportion were treated with cardioversion. Direct current cardioversion was performed twice as often as pharmacological cardioversion, and there appeared to be no major difference in outcome events for these two cardioversion modalities. For the overall cardioversion group, after adjustments for confounders, a significantly lower risk of mortality was found in patients who received early cardioversion compared with those who did not receive early cardioversion. STUDY REGISTRATION: ClinicalTrials.gov NCT01090362

    Identification of hot-spot residues in protein-protein interactions by computational docking

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    <p>Abstract</p> <p>Background</p> <p>The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.</p> <p>Results</p> <p>We have applied here normalized interface propensity (<it>NIP</it>) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex.</p> <p>Conclusion</p> <p>The <it>NIP </it>values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.</p
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