Uncomplicated acute type-B aortic dissection (ATBAD) is a misnomer because it has subgroups with excessive mortality risk. The Penn classification has designated these ATBAD presentations as class-A because they initially are characterized by the absence of malperfusion and/or aortic rupture. The Penn classification also has designated class-A high-risk subgroups as type I and low-risk subgroups as type II. The risk factors for Penn class-A type-I presentations relate to medical therapy; aortic anatomy, and dissection extent as outlined by the DeBakey classification. Tight medical therapy significantly protects against aortic complications. Beta-blockade, angiotensin inhibition, and calcium channel antagonists may reduce mortality. The details of optimal medical therapy require further research. The aortic risk factors for type-I presentations include false lumen size and patency, ulcer-like projections, aortic diameter >40 mm, and intimal tear characteristics such as size and proximal location. The prognostic role of dissection extent in ATBAD remains unclear, requiring further investigation to determine its effect on natural history. Future trials in Penn class-A ATBAD should focus on type-I presentations. The Penn classification can serve as a clinical framework for trial design, laying the groundwork for future management advances. It also may provide a common language to facilitate standardized definitions, trial design, and management approaches for this high-risk patient cohort.
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http://dx.doi.org/10.1053/j.jvca.2012.06.024 | DOI Listing |
Bull Math Biol
January 2025
Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark.
Using genetic data to infer evolutionary distances between molecular sequence pairs based on a Markov substitution model is a common procedure in phylogenetics, in particular for selecting a good starting tree to improve upon. Many evolutionary patterns can be accurately modelled using substitution models that are available in closed form, including the popular general time reversible model (GTR) for DNA data. For more complex biological phenomena, such as variations in lineage-specific evolutionary rates over time (heterotachy), other approaches such as the GTR with rate variation (GTR ) are required, but do not admit analytical solutions and do not automatically allow for likelihood calculations crucial for Bayesian analysis.
View Article and Find Full Text PDFJACC Heart Fail
January 2025
The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Background: Left ventricular (LV) dilatation and extensive scar portend a poor prognosis in heart failure (HF). The Revivent TC system (BioVentrix Inc) is used either during a hybrid transcatheter-surgical or a surgical-only procedure to exclude transmural scar and reduce LV dimensions.
Objectives: The purpose of this study was to examine the safety and efficacy of the Revivent TC® anchor system in patients with HF.
Sleep Health
January 2025
Department of Human and Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA.
Goal And Aims: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g.
View Article and Find Full Text PDFNeuro Oncol
January 2025
MacFeeters Hamilton Neuro-Oncology Program, Princess Margaret Cancer Centre, University Health Network and University of Toronto, Toronto, ON, Canada.
Background: Meningiomas exhibit considerable clinical and biological heterogeneity. We previously identified four distinct molecular groups (immunogenic, NF2-wildtype, hypermetabolic, proliferative) that address much of this heterogeneity. Despite the utility of these groups, the stochasticity of clustering methods and the use of multi-omics data for discovery limits the potential for classifying prospective cases.
View Article and Find Full Text PDFmedRxiv
December 2024
Critical Illness and Sepsis Research Center (CISRC), Penn State College of Medicine, Hershey, PA 17036, USA.
Objective: To determine whether neighborhood-level social determinants of health (SDoH) influence mortality following sepsis in the United States.
Study Setting And Design: Retrospective analysis of data from 4.4 million hospitalized patients diagnosed with sepsis, identified using International Classification of Diseases-10 codes, across the United States.
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