Jeffrey D. Helterbrand

Drotrecogin alfa (activated) administration across clinically important subgroups of patients with severe sepsis.

ObjectiveTo assess the effects of drotrecogin alfa (activated) therapy, a recombinant human activated protein C, across clinically relevant subpopulations in a randomized, phase 3, placebo-controlled study of patients with severe sepsis (recombinant human activated protein C worldwide evaluation in severe sepsis [PROWESS]). DesignUnivariate and multivariable analysis of prospectively defined subgroups from the PROWESS study. SettingA total of 164 medical centers in 11 countries. PatientsA total of 1,690 patients with severe sepsis. Measurements and Main ResultsWe report observed 28-day mortality rates for drotrecogin alfa (activated) and placebo patients for subgroups prospectively defined by demographic data, surgical status, type and site of infection, and clinical and biochemical measures of disease severity. We performed subgroup analyses to explore the consistency of the mortality benefit observed in the overall population and performed tests for both quantitative and qualitative interactions. To examine the magnitude of the treatment benefit with drotrecogin alfa (activated) across the underlying predicted risk of mortality spectrum, we used stepwise logistic regression on PROWESS placebo patients to generate a predicted risk of mortality model that simultaneously included many clinical and biochemical markers of mortality risk. Because drotrecogin alfa (activated) has anticoagulant properties, we also present analyses of bleeding and thrombotic events. Actual mortality rates were lower with drotrecogin alfa (activated) compared with placebo for nearly all prospectively defined subgroups. Both univariate and multivariable regression analyses showed a consistent relative risk reduction in 28-day mortality rates for drotrecogin alfa (activated). Larger absolute risk reductions were found with drotrecogin alfa (activated) in patients with a higher baseline predicted risk of mortality, and actual mortality rates were lower with drotrecogin alfa (activated) in all subgroups defined by disease severity measures where a ≥20% placebo mortality was observed. Although discriminatory power was limited by few observed events, the increased absolute risk of experiencing a serious bleeding event with treatment did not seem to vary according to the baseline predicted risk of mortality. ConclusionsThe administration of drotrecogin alfa (activated) to patients with severe sepsis was associated with a significant survival benefit that tended to increase with higher baseline likelihood of death. Current data suggest that the increased risk of bleeding does not vary according to likelihood of death.

Design and Methods of the Raloxifene Use for The Heart (RUTH) Study

Raloxifene is a selective estrogen receptor modulator that lowers total and low-density lipoprotein (LDL) cholesterol, reduces the risk of vertebral fracture, and is associated with a reduced incidence of invasive breast cancer in postmenopausal women with osteoporosis. The Raloxifene Use for The Heart (RUTH) trial is designed to determine whether raloxifene 60 mg/day compared with placebo: (1) lowers the risk of the coronary events (coronary death, nonfatal myocardial infarction [MI], or hospitalized acute coronary syndromes other than MI); and (2) reduces the risk of invasive breast cancer in women at risk for a major coronary event. RUTH is a double-blind, placebo-controlled, randomized clinical trial of 10,101 postmenopausal women aged > or =55 years from 26 countries. Women are eligible for randomization if they are postmenopausal and have documented coronary heart disease (CHD), peripheral arterial disease, or multiple risk factors for CHD. Use of estrogen within the previous 6 months is an exclusion factor. The study will be terminated after a minimum of 1,670 participants experience a primary coronary end point. Secondary end points include cardiovascular death, myocardial revascularization, noncoronary arterial revascularization, stroke, all-cause hospitalization, all-cause mortality, all breast cancers, clinical fractures, and venous thromboembolic events, in addition to the individual components of the composite primary coronary end point. RUTH will provide important information about the risk-benefit ratio of raloxifene in preventing acute coronary events and invasive breast cancer, as well as information about the natural history of CHD in women at risk of major coronary events.

Pharmacokinetic‐pharmacodynamic analysis of drotrecogin alfa (activated) in patients with severe sepsis

We aimed to characterize the pharmacokinetics and pharmacodynamics of drotrecogin alfa (activated) (recombinant human activated protein C) in patients with severe sepsis.

Universal cokriging under intrinsic coregionalization

Under the intrinsic coregionalization model if both primary and secondary measurements are available at all sample locations, the conventional geostatistical wisdom is that cokriging provides exactly the same solution as univariate kriging on the primary process alone. However, recent eamples have been given where nonzero secondary cokriging weights have accurred under this spatial dependence structure. This note identifies the conditions under which secondary information is useful under the assumption of intrinsic coregionalization. An illustration is given using a dataset of plutonium and americium concentrations collected from a region of the Nevada Test Site.

One-pixel-wide closed boundary identification

An appropriate space of one-pixel-wide closed (OPWC) boundary configurations is explicitly defined and an automatic algorithm to obtain OPWC contour estimates from a segmented image is presented. The motivation is to obtain a reasonable starting estimate for a Markov chain Monte Carlo-based (McMC-based) boundary optimization algorithm.

A statistical approach to identifying closed object boundaries in images

In this research, we present a statistical theory, and an algorithm, to identify one-pixel-wide closed object boundaries in gray-scale images. Closed-boundary identification is an important problem because boundaries of objects are major features in images. In spite of this, most statistical approaches to image restoration and texture identification place inappropriate stationary model assumptions on the image domain. One way to characterize the structural components present in images is to identify one-pixel-wide closed boundaries that delineate objects

Optimal closed boundary identification in gray-scale imagery

Identification of closed boundary contours is an important problem in image analysis because boundaries delineate the structural components, or objects, present in a scene. Most filter-based edge-detection methods do not have a mechanism to identify a group of edge sites that defines a complete closed object boundary. In this paper, we construct a suitable parameter space of one-pixel-wide closed boundaries for gray-scale images that reduces the complexity of the boundary identification problem. An algorithm based on stochastic processes and Bayesian methods is presented to identify an optimal boundary from this space. By defining a prior probability model and appropriately specifying transition probability functions on the space, a Markov chain Monte Carlo algorithm is constructed that theoretically converges to a statistically optimal closed boundary estimate. Moreover, this approach ensures that implementation via computer will result in a final boundary estimate that has the necessary property of closure which previous stochastic approaches have been unable to achieve.

Object identification using Markov random field segmentation models at multiple resolutions of a rectangular lattice

One of the most powerful uses for Markov random fields is in the area of image analysis, where the (noisy) image is observed on a rectangular lattice. In Bayesian approaches, Markov chain Monte Carlo (McMC) algorithms are usually suggested as a means to obtain a maximum a pos.. teriori (MAP) prediction. The particular problem we consider here is that of contextual image segmentation. In practice, approximations to theoretically optimal McMC algorithms are necessary but these algorithms tend to restrict movement through the space of potential segmentations. In this paper, efficient multi-resolution techniques are used to obtain a good initial labeling and to allow more movement through the label configuration space. Examples of both natural images and synthetic images are presented.

Stochastic recognition of closed object boundaries in images

Engineering-based edge detection techniques generally use local intensity information to identify whether a pixel location is part of a boundary. Boundaries are presumed present where sharp transitions in the observed intensities occur. Unfortunately, these approaches are sensitive to error and hidden partial boundaries, which hinder the determination of closed object boundaries. In this research, a method to obtain statistically optimal closed object boundaries is presented.