Supplementary Material Online Research Articles

Reversal strategies for non-vitamin K antagonist oral anticoagulants: a critical appraisal of available evidence and recommendations for clinical management-a joint position paper of the European Society of Cardiology Working Group on Cardiovascular Pharmacotherapy and European Society of Cardiology Working Group on Thrombosis.

Bleeding is the main adverse effect when using oral anticoagulants (OACs). The risk of major bleeding with vitamin K antagonists (VKAs) is dependent on the quality of anticoagulation control and estimated to be ∼1.3/100 patients/year in patients with INR 2.0–3.0.1,2 Multiple limitations of VKAs stimulated the development of non-vitamin K antagonist oral anticoagulants (NOACs) acting directly on coagulation factors, including factor FIIa (thrombin) and FXa. Pharmacological properties of the NOACs relevant for bleeding are summarized in the supplement including Supplementary material online, Table S1 and their main effects are summarized in Supplementary material online, Table S2 . The risk of bleeding in patients managed with NOACs was properly assessed in the four landmark Phase III trials in patients with atrial fibrillation (AF; Table 1 ). Major bleeding was the principal safety outcome in RE-LY,3,4 ARISTOTLE,5 and ENGAGE AF.6 In ROCKET-AF, the primary safety endpoint was the composite of major and non-major clinically relevant bleeding.7 The risk of major bleeding was significantly reduced with dabigatran 110 mg b.i.d., apixaban and both doses of edoxaban3–6 while the bleeding rates for dabigatran 150 mg b.i.d. and rivaroxaban were similar to those with warfarin ( Table 1 ). View this table: Table 1 Risk of bleeding with non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation (rates per 100 patients-years) Gastrointestinal (GI) bleeding accounts for ∼90% of major extracranial haemorrhages in patients with AF receiving VKAs.8 With the NOACs, dabigatran 150 mg b.i.d., rivaroxaban, and edoxaban 60 mg o.d. significantly increased rates of GI bleeding (∼1.5-fold) compared with warfarin.3,6,7 While rivaroxaban increases upper and lower GI bleeding …

Empirical Bayes analysis of RNA-seq data for detection of gene expression heterosis.

An important type of heterosis, known as hybrid vigor, refers to the enhancements in the phenotype of hybrid progeny relative to their inbred parents. Although hybrid vigor is extensively utilized in agriculture, its molecular basis is still largely unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers are measuring transcript abundance levels of thousands of genes in parental inbred lines and their hybrid offspring using RNA sequencing (RNA-seq) technology. The resulting data allow researchers to search for evidence of gene expression heterosis as one potential molecular mechanism underlying heterosis of agriculturally important traits. The null hypotheses of greatest interest in testing for gene expression heterosis are composite null hypotheses that are difficult to test with standard statistical approaches for RNA-seq analysis. To address these shortcomings, we develop a hierarchical negative binomial model and draw inferences using a computationally tractable empirical Bayes approach to inference. We demonstrate improvements over alternative methods via a simulation study based on a maize experiment and then analyze that maize experiment with our newly proposed methodology. This article has supplementary material online.

Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote

Placing sensors in every station of a process or every element of a system to monistor its state or performance is usually too expensive or physically impossible. Therefore, a systematic method is needed to select important sensing variables. The method should not only be capable of identifying important sensors/signals among multistream signals from a distributed sensing system, but should also be able to extract a small set of interpretable features from the high-dimensional vector of a selected signal. For this purpose, we develop a new hierarchical regularization approach called hierarchical nonnegative garrote (NNG). At the first level of hierarchy, a group NNG is used to select important signals, and at the second level, the individual features within each signal are selected using a modified version of NNG that possesses good properties for the estimated coefficients. Performance of the proposed method is evaluated and compared with other existing methods through Monte Carlo simulation. A case study is conducted to demonstrate the proposed methodology that can be applied to develop a predictive model for the assessment of vehicle design comfort based on the tested drivers’ motion trajectory signals. This article has supplementary material online.

A Semiparametric Software Reliability Model for Analysis of a Bug-Database With Multiple Defect Types

Software bug-databases provide an important source of data for assessing the reliability of a software product after its release. Statistical analysis of these databases can be challenging when software usage is unknown, that is, there is no information about the usage, either in the form of a parametric model, or in the form of actual measurements. Reliability metrics, when defined on a calendar time scale, would depend on this unknown and time-dependent usage of the software and hence cannot be estimated. This article proposes a semiparametric analysis that makes use of defect classifications into multiple types to enable estimation of a model without making strict assumptions about the underlying usage of the software. New reliability metrics whose computation does not depend on the unknown usage of the software have been proposed and methods for estimating them have been developed. The proposed method has been illustrated using data retrieved from the bug-database of a popular scripting language, named Python. This illustration compares reliability of two versions of the software without making assumptions about their unknown usage. This article has supplementary material online.

Are You Normal? The Problem of Confounded Residual Structures in Hierarchical Linear Models

We encounter hierarchical data structures in a wide range of applications. Regular linear models are extended by random effects to address correlation between observations in the same group. Inference for random effects is sensitive to distributional misspecifications of the model, making checks for (distributional) assumptions particularly important. The investigation of residual structures is complicated by the presence of different levels and corresponding dependencies. Ignoring these dependencies leads to erroneous conclusions using our familiar tools, such as Q–Q plots or normal tests. We first show the extent of the problem, then we introduce the fraction of confounding as a measure of the level of confounding in a model and finally introduce rotated random effects as a solution to assessing distributional model assumptions. This article has supplementary materials online.

Stochastic Polynomial Interpolation for Uncertainty Quantification With Computer Experiments

Multivariate polynomials are increasingly being used to construct emulators of computer models for uncertainty quantification. For deterministic computer codes, interpolating polynomial metamodels should be used instead of noninterpolating ones for logical consistency and prediction accuracy. However, available methods for constructing interpolating polynomials only provide point predictions. There is no known method that can provide probabilistic statements about the interpolation error. Furthermore, there are few alternatives to grid designs and sparse grids for constructing multivariate interpolating polynomials. A significant disadvantage of these designs is the large gaps between allowable design sizes. This article proposes a stochastic interpolating polynomial (SIP) that seeks to overcome the problems discussed above. A Bayesian approach in which interpolation uncertainty is quantified probabilistically through the posterior distribution of the output is employed. This allows assessment of the effect of interpolation uncertainty on estimation of quantities of interest based on the metamodel. A class of transformed space-filling design and a sequential design approach are proposed to efficiently construct the SIP with any desired number of runs. Simulations demonstrate that the SIP can outperform Gaussian process (GP) emulators. This article has supplementary material online.

General Blending Models for Data From Mixture Experiments

We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online.

Syncope clinical management in the emergency department: a consensus from the first international workshop on syncope risk stratification in the emergency department

The optimal emergency department (ED) evaluation of syncope is uncertain. Research reports from multiple countries suggest extensive practice variation, high costs, and questionable benefit associated with current approaches.1–5 Moreover, only a few of the recommendations from international syncope guidelines deal with ED management.6–8 For example, the European Society of Cardiology guidelines, which are the most inclusive syncope guidelines, do not address the ED management. This could be due to limited evidence on how to stratify the risk and decide on disposition of these patients in the ED.1,9 We organized a multi-specialty workshop of North American and European syncope experts on 26–27 September 2013 in Gargnano, Italy, with the aim of obtaining a modified Delphi consensus on the best way to manage ED syncope patients. As already described,10 we followed a four-step conceptual model for the ED decision-making in syncope: (i) Is it syncope? (ii) Is there a serious underlying condition identified in the ED? (iii) If the cause is uncertain, what is the risk of a serious outcome? (iv) For a given risk profile, how can these patients be best managed in the ED and what evaluation and restrictions are required? ( Figure 1 ) . Figure 1 Conceptual model: ED management of syncope. Expert recruitment and consensus development have been described previously.10 Details can be found in Supplementary material online, Appendix S1 . The full list of questions and answers to the first and second survey rounds as well as the degree of agreement on each item is reported in Supplementary material online, Appendix S2 . According to the ESC guidelines, syncope is defined as a …

Cokriging-Based Sequential Design Strategies Using Fast Cross-Validation Techniques for Multi-Fidelity Computer Codes

Cokriging-based surrogate models have become popular in recent decades to approximate a computer code output from a few simulations using both coarse and more complex versions of the code. In practical applications, it is common to sequentially add new simulations to obtain more accurate approximations. We propose a method of cokriging-based sequential design, which combines both the error evaluation provided by the cokriging model and the observed errors of a leave-one-out cross-validation procedure. This method is proposed in two versions, the first one selects points one at a time. The second one allows us to parallelize the simulations and to add several design points at a time. The main advantage of the suggested strategies is that at a new design point they choose which code versions should be simulated (i.e., the complex code or one of its fast approximations). A multifidelity application is used to illustrate the efficiency of the proposed approaches. In this example, the accurate code is a two-dimensional finite element model and the less accurate one is a one-dimensional approximation of the system. This article has supplementary material online.

Recommendations on pre-hospital and early hospital management of acute heart failure: a consensus paper from the Heart Failure Association of the European Society of Cardiology, the European Society of Emergency Medicine and the Society of Academic Emergency Medicine--short version.

Despite several critical steps forward in the management of chronic heart failure (CHF), the area of acute heart failure (AHF) has remained relatively stagnant. As stated in the updated ESC HF guidelines, clinicians responsible for managing patients with AHF must frequently make treatment decisions without adequate evidence, usually on the basis of expert opinion consensus.2 Specifically, the treatment of acute HF remains largely opinion-based with little good evidence to guide therapy. Acute heart failure is a syndrome in which emergency physicians, cardiologists, intensivists, nurses, and other healthcare providers have to cooperate to provide ‘rapid’ benefit to the patients. We hereby would like to underscore the wider experience grown in different settings of the area of intensive care on acute heart failure, actually larger and more composite than that got in specialized Care Units. The distillate of such different experiences is discussed and integrated in the present document. Hence, the authors of this consensus paper believe a common working definition of AHF covering all dimensions and modes of presentations has to be made, with the understanding that most AHF presentations are either acute decompensations of chronic underlying HF or the abrupt onset of dyspnoea associated with significantly elevated blood pressure. Secondly, recent data show that, much like acute coronary syndrome, AHF might have a ‘time to therapy’ concept. Accordingly, ‘pre-hospital’ management is considered a critical component of care. Thirdly, most patients with AHF have normal or high blood pressure at presentation, and are admitted with symptoms and/or signs of congestion. This is in contradiction to the presentation where low cardiac output leads to symptomatic hypotension and signs/symptoms of hypoperfusion, a circumstance that is relatively rare, present in coronary care unit/intensive care unit (CCU/ICU) but associated with a particularly poor outcome. Hence, it is important to note that appropriate therapy requires appropriate identification of the specific AHF phenotype.3 The aim of the current paper is not to replace guidelines, but, to provide contemporary perspective for early hospital management within the context of the most recent data and to provide guidance, based on expert opinions, to practicing physicians and other healthcare professionals (Figure 1). We believe that the experience accrued in the different settings from the emergency department through to the ICU/CCU is collectivel valuable in determining how best to manage the patients with AHF. Herein, a shortened version mainly including group recommendations is provided. Full version of the consensus paper is provided as Supplementary material online.

A reply to ‘A meta-database of Holocene sediment cores for England: missing data’ (Tooley 2015)

We welcome the response of Tooley (2015) to our article describing a new meta-database of Holocene sediment cores for England. In our article we describe the online publication of this meta-database, arising from systematic meta-search. We define its scope and the meta-data it contains, before providing the data themselves (in the Electronic Supplementary Material online). We note that Prof. Tooley describes the idea of such a database as important and valuable, and we welcome the constructive approach he adopts throughout his article. Tooley highlights that the meta-database can be enhanced by the inclusion of a number of studies of the Coastal Lowlands, highlighting gaps in the Lancashire and Hartlepool Bay areas in particular. While it is undoubtedly true that these studies were omitted, they tend to document boreholes which have shown Holocene sediments, rather than boreholes subject to the analysis of least one palaeoecological proxy, as per our inclusion criterion. For example, based on the information M.J. Tooley provides, we estimate that 17 such analyses from Lancashire would have satisfied this criterion. It is certainly clear that these omissions are genuine, and we would agree that they add to the pool of sites already described in the meta-database. Because of the constraints of systematic search however, it could also be the case that omissions exist outside these areas, and in the original text we highlighted that: ‘‘the resulting meta-database is by no means exhaustive and we would expect further additions to be made in due course’’. We therefore welcome this addition and would similarly do so for others highlighted to the author team. We would however contest the suggestion that ‘much’ of the published data have been overlooked from improper searching. Tooley implores a greater level of focus at the county level; we would only encourage consideration of the attendant effects of his proposed strategy on search volume (the modern counties of England would generate an 84 fold increase to our list of

Classic cor triatriatum with opposing shunts at the atrial level: a rare association

A 6-year-old girl presented with progressive dyspnoea and cyanosis. Examination revealed grade 1 clubbing, cyanosis (saturation of 85%), right ventricular (RV) heave, and loud S2. ECG and chest radiograph showed evidence of RV pressure overload (Supplementary material online, Figures S1 and S2 ). Transthoracic echocardiography demonstrated a membrane dividing the left atrium (LA) into a proximal accessory chamber (AC) and a distal main chamber, consistent with classic cor triatriatum ( Panel A — parasternal long …

The current role of next-generation DNA sequencing in routine care of patients with hereditary cardiovascular conditions: a viewpoint paper of the European Society of Cardiology working group on myocardial and pericardial diseases and members of the European Society of Human Genetics.

Cardiomyopathies, arrhythmic syndromes, aortopathies, and other cardiovascular diseases with Mendelian inheritance are relatively frequent conditions for which genetic testing is recommended in various guidelines.1,2 The most widely recognized indication for genetic testing in patients with these conditions is to identify a causative mutation and subsequently provide pre-symptomatic or predictive testing of relatives who are at risk of developing the same disease at a later stage. This process of cascade screening of family members ensures adequate clinical surveillance of mutation carriers and allows non-carriers to be discharged from clinical follow-up. A number of studies have reported a greater cost-effectiveness combining molecular screening with clinical screening compared with isolated clinical investigations.3 Previously, genetic testing was based on conventional techniques like Sanger sequencing analysing genes one by one, but recent advances in DNA sequencing technologies have made it possible to investigate large numbers of disease genes simultaneously, making mutation analysis much faster and cheaper. These new methods are known as next-generation sequencing (NGS) and represent a major advance in the ability to identify causative mutations in families affected by genetic diseases (see Supplementary material online, Figure S1 ).4,5 However, analysis of large numbers of genes may identify a number of sequence variants of uncertain clinical significance (VUS). As a result, cardiologists and clinical geneticists who counsel and manage families with inherited cardiovascular disorders are facing a major challenge in determining the clinical relevance of NGS results.6,7 This paper gives a brief overview of the principles of NGS, discusses the general strategies for the interpretation of sequencing results, and reviews the implications of NGS for cardio-genetic services. In addition, …

Engineering-Driven Statistical Adjustment and Calibration

Engineering model development involves several simplifying assumptions for the purpose of mathematical tractability, which are often not realistic in practice. This leads to discrepancies in the model predictions. A commonly used statistical approach to overcome this problem is to build a statistical model for the discrepancies between the engineering model and observed data. In contrast, an engineering approach would be to find the causes of discrepancy and fix the engineering model using first principles. However, the engineering approach is time consuming, whereas the statistical approach is fast. The drawback of the statistical approach is that it treats the engineering model as a black box and therefore, the statistically adjusted models lack physical interpretability. This article proposes a new framework for model calibration and statistical adjustment. It tries to open up the black box using simple main effects analysis and graphical plots and introduces statistical models inside the engineering model. This approach leads to simpler adjustment models that are physically more interpretable. The approach is illustrated using a model for predicting the cutting forces in a laser-assisted mechanical micro-machining process. This article has supplementary material online.

Statistical Methods for Degradation Data With Dynamic Covariates Information and an Application to Outdoor Weathering Data

Degradation data provide a useful resource for obtaining reliability information for some highly reliable products and systems. In addition to product/system degradation measurements, it is common nowadays to dynamically record product/system usage as well as other life-affecting environmental variables, such as load, amount of use, temperature, and humidity. We refer to these variables as dynamic covariate information. In this article, we introduce a class of models for analyzing degradation data with dynamic covariate information. We use a general path model with individual random effects to describe degradation paths and a vector time series model to describe the covariate process. Shape-restricted splines are used to estimate the effects of dynamic covariates on the degradation process. The unknown parameters in the degradation data model and the covariate process model are estimated by using maximum likelihood. We also describe algorithms for computing an estimate of the lifetime distribution induced by the proposed degradation path model. The proposed methods are illustrated with an application for predicting the life of an organic coating in a complicated dynamic environment (i.e., changing UV spectrum and intensity, temperature, and humidity). This article has supplementary material online.

The Mahalanobis Distance for Functional Data With Applications to Classification

This article presents a new semidistance for functional observations that generalizes the Mahalanobis distance for multivariate datasets. The main characteristics of the functional Mahalanobis semidistance are shown. To illustrate the applicability of this measure of proximity between functional observations, new versions of several well-known functional classification procedures are developed using the functional Mahalanobis semidistance. A Monte Carlo study and the analysis of two real examples indicate that the classification methods used in conjunction with the functional Mahalanobis semidistance give better results than other well-known functional classification procedures. This article has supplementary material online.

Acute cocaine myocarditis: a word of caution

An otherwise healthy 25-year-old male presented to the Emergency Department with new onset severe retrosternal chest pain. Electrocardiogram demonstrated lateral ST-segment elevations in leads I and AVL with inferior ST-segment depression ( Panel A ). Initial troponin I level was 1.8 ng/mL quickly rising to >200 ng/mL within first 8 h. Coronary angiography revealed normal coronary arteries (see Supplementary material online, Video S1 ). Echocardiogram showed regional wall motion abnormalities involving inferolateral segment with moderately reduced left ventricular ejection fraction …

Sequential Exploration of Complex Surfaces Using Minimum Energy Designs

A new space-filling design, called minimum energy design (MED), is proposed to explore unknown regions of the design space of particular interest to an experimenter. The key ideas involved in constructing the MED are the visualization of each design point as a charged particle inside a box, and minimization of the total potential energy of these particles. It is shown through theoretical arguments and simulations that with a proper choice of the charge function, the MED can asymptotically generate any arbitrary probability density function. A version of the MED, which adaptively updates the design by “learning” about the unknown response surface sequentially, is proposed and implemented. Two potential applications of MED in simulation of complex probability densities and optimization of complex response surfaces are discussed and demonstrated with examples. This article has supplementary material online.

Visualizing Complex Data With Embedded Plots

This article describes a class of graphs, embedded plots, that are particularly useful for analyzing large and complex datasets. Embedded plots organize a collection of graphs into a larger graphic, which can display more complex relationships than would otherwise be possible. This arrangement provides additional axes, prevents overplotting, and allows for multiple levels of visual summarization. Embedded plots also preprocess complex data into a form suitable for the human cognitive system, which can facilitate comprehension. We illustrate the usefulness of embedded plots with a case study, discuss the practical and cognitive advantages of embedded plots, and demonstrate how to implement embedded plots as a general class within visualization software, something currently unavailable. This article has supplementary material online.

Dynamic Retrospective Regression for Functional Data

Samples of curves, or functional data, usually present phase variability in addition to amplitude variability. Existing functional regression methods do not handle phase variability in an efficient way. In this article we propose a functional regression method that incorporates phase synchronization as an intrinsic part of the model, and then attains better predictive power than ordinary linear regression in a simple and parsimonious way. The finite-sample properties of the estimators are studied by simulation. As an example of application, we analyze neuromotor data arising from a study of human lip movement. This article has supplementary materials online.