Introduction: Balloon-expandable (BE) and self-expandable (SE) prostheses are the main types of devices currently used in transcatheter aortic valve implantation (TAVI). Despite the different designs, clinical practice guidelines do not make any specific recommendation on the selection of one device over the other. Most operators are trained in using both BE and SE prostheses, but operator experience with each of the two designs might influence patient outcomes. The aim of this study was to compare the immediate and mid-term clinical outcomes during the learning curve in BE versus SE TAVI.
Methods: The transfemoral TAVI procedures performed in a single center between July 2017 and March 2021 were grouped according to the type of implanted prosthesis. The procedures in each group were ordered according to the case sequence number. For each patient, a minimum follow-up time of 12 months was required for inclusion in the analysis. The outcomes of the BE TAVI procedures were compared with the outcomes of the SE TAVI procedures. Clinical endpoints were defined according to the Valve Academic Research Consortium 3 (VARC-3).
Results: The median follow-up time was 28 months. Each device group included 128 patients. In the BE group, case sequence number predicted mid-term all-cause mortality at an optimal cutoff value ≤58 procedures (AUC 0.730; 95% CI: 0.644-0.805; p < 0.001), while in the SE group, the cutoff value was ≤85 procedures (AUC 0.625; 95% CI: 0.535-0.710; p = 0.04). A direct comparison of the AUC showed that case sequence number was equally adequate in predicting mid-term mortality, irrespective of prosthesis type (p = 0.11). A low case sequence number was associated with an increased rate of VARC-3 major cardiac and vascular complications (OR 0.98 95% CI: 0.96-0.99; p = 0.03) in the BE device group, and with an increased rate of post-TAVI aortic regurgitation ≥ grade II (OR 0.98; 95% CI: 0.97-0.99; p = 0.03) in the SE device group.
Conclusions: In transfemoral TAVI, case sequence number influenced mid-term mortality irrespective of prosthesis type, but the learning curve was longer in the case of SE devices.
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http://dx.doi.org/10.1159/000531401 | DOI Listing |
Chaos
January 2025
Division of Control and Dynamical Systems, Instituto Potosino de Investigación Científica y Tecnológica, Camino a la Presa San José 2055, Col. Lomas 4ta. Sección, 78216 San Luis Potosí, SLP, México.
In this paper, we give a class of one-dimensional discrete dynamical systems with state space N+. This class of systems is defined by two parameters: one of them sets the number of nearest neighbors that determine the rule of evolution, and the other parameter determines a segment of natural numbers Λ={1,2,…,b}. In particular, we investigate the behavior of a class of one-dimensional maps where an integer moves to an other integer given by the sum of the nearest neighbors minus a multiple of b∈N+.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Background: Several viruses have been linked to Alzheimer disease (AD) by independent lines of evidence.
Method: Whole genome and whole exome sequences (WGS/WES) derived from brain (3,404 AD cases, 894 controls) and blood (15,612 AD cases, 24,544 controls) obtained from European ancestry (EU), African American (AA), Mexican (HMX), South Asian Indian (IND), and Caribbean Hispanic (CH) participants of the Alzheimer's Disease Sequencing Project (ADSP) and 276 AD cases 3,584 controls (all EU) from the Framingham Heart Study (FHS) that did not align to the human reference genome were aligned to viral reference genomes. A genome-wide association study (GWAS) for viral DNA load was conducted using PLINK software and regression models with covariates for sex, age, ancestry principal components, and tissue source.
Alzheimers Dement
December 2024
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: Genome-wide association studies (GWAS) in Alzheimer's disease (AD) leveraging endophenotypes beyond case/control diagnosis, such as brain amyloid β pathology, have shown promise in identifying novel variants and understanding their potential functional impact. In this study, we leverage two brain amyloid β pathology measurement modalities, PET imaging and neuropathology, to address sample size limitations and to discover novel genetic drivers of disease.
Method: We conducted a meta-analysis on an amyloid PET imaging GWAS (N = 7,036, 35% amyloid positive, 53.
Alzheimers Dement
December 2024
School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
Background: The common APOE2/E3/E4 polymorphism, the strongest risk factor for Alzheimer's disease (AD), is determined by two-site haplotypes at codons 112 (Cys>Arg) and 158 (Arg>Cys), resulting into six genotypes. Due to strong linkage disequilibrium between the two sites, 3 of the 4 expected haplotypes (E2, E3, E4) have been observed and extensively studied in relation to AD risk. Compared to the most common haplotype of E3 (Cys112 - Arg158), E4 (Arg112 - Arg 158) and E2 (Cys112 - Cys158) haplotypes are determined by a single-point mutation at codons 112 and 158, respectively.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
Background: Although high-throughput DNA/RNA sequencing technologies have generated massive genetic and genomic data in human disease, translation of these findings into new patient treatment has not materialized by lack of effective approaches, such as Artificial Intelligence (AL) and Machine Learning (ML) tools.
Method: To address this problem, we have used AI/ML approaches, Mendelian randomization (MR), and large patient's genetic and functional genomic data to evaluate druggable targets using Alzheimer's disease (AD) as a prototypical example. We utilized the genomic instruments from 9 expression quantitative trait loci (eQTL) and 3 protein quantitative trait loci (pQTL) datasets across five human brain regions from three biobanks.
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