Prehospital use of plasma in traumatic hemorrhage (The PUPTH Trial): study protocol for a randomised controlled trial

Background Severe traumatic injury and haemorrhagic shock are frequently associated with disruptions of coagulation function (such as trauma-induced coagulopathy TIC) and activation of inflammatory cascades. These pathologies may be exacerbated by current standard of care resuscitation protocols. Observational studies suggest early administration of plasma to severely-injured haemorrhaging patients may correct TIC, minimise inflammation, and improve survival. The proposed randomised clinical trial will evaluate the clinical effectiveness of pre-hospital plasma administration compared with standard- of-care crystalloid resuscitation in severely-injured patients with major traumatic haemorrhage. Methods/design This is a prospective, randomized, open-label, non-blinded trial to determine the effect of pre-hospital administration of thawed plasma (TP) on mortality, morbidity, transfusion requirements, coagulation, and inflammatory response in severely-injured bleeding trauma patients. Two hundred and ten eligible adult trauma patients will be randomised to receive either two units of plasma, to be administered in-field, vsstandard of care normal saline (NS). Main analyses will compare subjects allocated to TP to those allocated to NS, on an intention-to-treat basis. Primary outcome measure is all-cause 30-day mortality. Secondary outcome measures include coagulation and lipidomic/pro-inflammatory marker responses, volume of resuscitation fluids (crystalloid, colloid) and blood products administered, and major hospital outcomes (e.g. incidence of MSOF, length of ICU stay, length of hospital stay). Discussion This study is part of a US Department of Defense (DoD)-funded multi-institutional investigation, conducted independently of, but in parallel with, the University of Pittsburgh and University of Denver. Demonstration of significant reductions in mortality and coagulopathic/inflammatory-related morbidities as a result of pre-hospital plasma administration would be of considerable clinical importance for the management of haemorrhagic shock in both civilian and military populations. Trial registration ClinicalTrials.gov: NCT02303964 on 28 November 201

Variables associated with odds of finishing and finish time in a 161-km ultramarathon

We sought to determine the degree to which age, sex, calendar year, previous event experience and ambient race day temperature were associated with finishing a 100-mile (161-km) trail running race and with finish time in that race. We computed separate generalized linear mixed-effects regression models for (1) odds of finishing and (2) finish times of finishers. Every starter from 1986 to 2007 was used in computing the models for odds of finishing (8,282 starts by 3,956 individuals) and every finisher in the same period was included in the models for finish time (5,276 finishes). Factors associated with improved odds of finishing included being a first-time starter and advancing calendar year. Factors associated with reduced odds of finishing included advancing age above 38 years and warmer weather. Beyond 38 years of age, women had worse odds of finishing than men. Warmer weather had a similar effect on finish rates for men and women. Finish times were slower with advancing age, slower for women than men, and less affected by warm weather for women than for men. Calendar year was not associated with finish time after adjustment for other variables

Latent models for cross-covariance

Thesis (Ph. D.)--University of Washington, 2001Cross-covariance problems arise in the analysis of multivariate data that can be divided naturally into two blocks of variables, X and Y, observed on the same units. In a cross-covariance problem we are interested, not in the within-block covariances, but in the way the Ys vary with the Xs.In the current work several approaches to the cross-covariance problem are discussed, including Reduced-Rank Regression (RRR), Canonical Correlation Analysis (CCA), Partial Least Squares (PLS, also called Projection to Latent Structures), Structural Equation Models (SEM), and Graphical Markov Models (GMM).A family of latent models for cross-covariance, called paired latent models, is specified. It is shown that the set of covariance matrices which can be modeled under the rank-r paired latent model is the same as those which can be modeled under rank-r Reduced-Rank Regression. The degree to which the parameters of the rank-one paired latent model are underidentified is precisely characterized, and a natural convention is proposed which makes the model identifiable. This result has implications for the estimation of correlation between the latent variables.It is shown that symmetric and asymmetric versions of the paired latent model are covariance equivalent, and that this equivalence fails when the within-block covariance is constrained to be diagonal

Latent models for cross-covariance

We consider models for the covariance between two blocks of variables. Such models are often used in situations where latent variables are believed to present. In this paper we characterize exactly the set of distributions given by a class of models with one-dimensional latent variables. These models relate two blocks of observed variables, modeling only the cross-covariance matrix. We describe the relation of this model to the singular value decomposition of the cross-covariance matrix. We show that, although the model is underidentified, useful information may be extracted. We further consider an alternative parameterization in which one latent variable is associated with each block, and we extend the result to models with r-dimensional latent variables.Canonical correlation Latent variables Partial least squares Reduced-rank regression Singular value decomposition

Contents

A class of Gaussian latent-variable models for cross-covariance is specified, and the set of distributions over the observed variables to which they correspond is precisely characterized. In this class the observed variables, or indicators, are divided into two blocks, X and Y. A pair of latent variables is postulated, one for each block, ξ for X and ω for Y. The indicators are linear functions of their respective latent variables plus error, and errors for the X block are uncorrelated with those of the Y block. This latent-variable model differs from the well-known exploratory factor model in that the within-block covariances of the errors are unconstrained. Any variance-covariance matrix over the indicators with rank(ΣXY) = 1 can be fit exactly by the latent-variable model. Although the model is underidentified, the linear coefficient vectors a, linking ξ to X, and b, linking ω to Y, are identified up to sign and scale. Cor (ξ, ω) = 1 is always feasible, and |Cor (ξ, ω) | is bounded below. When |Cor (ξ, ω) | attains its minimum, the scales of a and b are maximized and withinblock errors are minimized. Subject to the constraint that |Cor (ξ, ω) | is at its minimum, the model is identified up to sign