Ventricular endocardial tissue geometry influences stimulus threshold and effective refractory period

Understanding the biophysical processes by which electrical stimuli applied to cardiac tissue may result in local activation is important in both the experimental and clinical electrophysiology laboratory environments, as well as gaining a more in-depth knowledge of the mechanisms of focal trigger-induced arrhythmias. Previous computational models have predicted that local myocardial tissue architecture alone may significantly modulate tissue excitability, affecting both the local stimulus current required to excite the tissue and the local effective refractory period (ERP).

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Clinical Validation of the Comprehensive Complication Index as a Measure of Postoperative Morbidity at a Surgical Department: A Prospective Study

Objective: Using clinical outcomes, to validate the comprehensive complication index (CCI) as a measure of postoperative morbidity in all patients undergoing surgery at a general surgery department. Background: The Clavien-Dindo classification (CDC) is the most widely used system to assess postoperative morbidity. The CCI is a numerical scale based on the CDC. Once validated, it could be used universally to establish and compare the real postoperative complications of each surgical procedure. Methods: Observational prospective cohort study. All patients who underwent surgery during the 1-year study period were included. All the complications graded with the CDC and related to the initial admission, or until discharge if the patient was readmitted within 90 days of surgery, were included. Surgical procedures were classified according to the operative severity score (OSS) as minor, moderate, major, or major+. The clinical validation of the CCI was performed by assessing its correlation with 4 different clinical outcomes. Results: A total of 1850 patients were included: 513 (27.7%) presented complications and 101 (5.46%) were readmitted. In the multivariate analysis, the CCI and CDC were associated with postoperative stay, prolongation of postoperative stay, readmission, and disability in all OSS groups (P

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Principal ignorability in mediation analysis: through and beyond sequential ignorability

Summary

In causal mediation analysis, the definitions of the natural direct and indirect effects involve potential outcomes that can never be observed, so-called a priori counterfactuals. This conceptual challenge translates into issues in identification, which requires strong and often unverifiable assumptions, including sequential ignorability. Alternatively, we can deal with post-treatment variables using the principal stratification framework, where causal effects are defined as comparisons of observable potential outcomes. We establish a novel bridge between mediation analysis and principal stratification, which helps to clarify and weaken the commonly used identifying assumptions for natural direct and indirect effects. Using principal stratification, we show how sequential ignorability extrapolates from observable potential outcomes to a priori counterfactuals, and propose alternative weaker principal ignorability-type assumptions. We illustrate the key concepts using a clinical trial.

https://ift.tt/2z16lnk

A convex formulation for high-dimensional sparse sliced inverse regression

Summary

Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of information on the conditional distribution of the response given the covariates. The estimated linear combinations include all covariates, making results difficult to interpret and perhaps unnecessarily variable, particularly when the number of covariates is large. In this paper, we propose a convex formulation for fitting sparse sliced inverse regression in high dimensions. Our proposal estimates the subspace of the linear combinations of the covariates directly and performs variable selection simultaneously. We solve the resulting convex optimization problem via the linearized alternating direction methods of multiplier algorithm, and establish an upper bound on the subspace distance between the estimated and the true subspaces. Through numerical studies, we show that our proposal is able to identify the correct covariates in the high-dimensional setting.

https://ift.tt/2RKUtfR

Statistical sparsity

Summary

The main contribution of this paper is a mathematical definition of statistical sparsity, which is expressed as a limiting property of a sequence of probability distributions. The limit is characterized by an exceedance measure $H$ and a rate parameter $\rho > 0$, both of which are unrelated to sample size. The definition encompasses all sparsity models that have been suggested in the signal-detection literature. Sparsity implies that $\rho$ is small, and a sparse approximation is asymptotic in the rate parameter, typically with error $o(\rho)$ in the sparse limit $\rho \to 0$. To first order in sparsity, the sparse signal plus Gaussian noise convolution depends on the signal distribution only through its rate parameter and exceedance measure. This is one of several asymptotic approximations implied by the definition, each of which is most conveniently expressed in terms of the zeta transformation of the exceedance measure. One implication is that two sparse families having the same exceedance measure are inferentially equivalent and cannot be distinguished to first order. Thus, aspects of the signal distribution that have a negligible effect on observables can be ignored with impunity, leaving only the exceedance measure to be considered. From this point of view, scale models and inverse-power measures seem particularly attractive.

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Integrative linear discriminant analysis with guaranteed error rate improvement

Summary

Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer's disease.

https://ift.tt/2RI5QW0

The change-plane Cox model

Summary

We propose a projection pursuit technique in survival analysis for finding lower-dimensional projections that exhibit differentiated survival outcomes. This idea is formally introduced as the change-plane Cox model, a nonregular Cox model with a change-plane in the covariate space that divides the population into two subgroups whose hazards are proportional. The proposed technique offers a potential framework for principled subgroup discovery. Estimation of the change-plane is accomplished via likelihood maximization over a data-driven sieve constructed using sliced inverse regression. Consistency of the sieve procedure for the change-plane parameters is established. In simulations the sieve estimator demonstrates better classification performance for subgroup identification than alternatives.

https://ift.tt/2z26ZRi