PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 17057381

Mild left ventricular dysfunction is associated with thrombogenicity in cardioembolic stroke.

Hyung-Min Kwon·Jong-Ho Park·Jeong-Min Kim·Byung-Woo Yoon
View original source ↗Export metadata ↓

Abstract

BACKGROUND: Transesophageal echocardiography (TEE) has been recognized as a valuable tool for identifying the left cardiac thrombus (LCT) or spontaneous echocardiographic contrast (SEC). We aimed to identify risk groups where TEE should be performed in patients with suspected cardioembolic stroke according to magnetic resonance imaging analysis. METHODS: One hundred and forty-six patients (mean age 64.7 +/- 11.8 years, 101 males) with suspected cardioembolic stroke were analyzed. We used TEE for the presence of LCT and/or SEC as indication of thrombogenicity. We evaluated the association between thrombogenicity and demographic features, stroke risk factors and echocardiographic variables. RESULTS: The study included 40 patients (27.3%) who showed thrombogenicity (10 LCT and 30 SEC). The independent echocardiographic variables of thrombogenicity were atrial fibrillation (OR 7.14; 95% CI 2.62-19.48; p < 0.001) and left ventricular ejection fraction </=50% (OR 4.01; 95% CI 1.40-11.49; p = 0.01). CONCLUSION: We recommend that TEE should be considered in patients with mildly reduced left ventricular dysfunction (ejection fraction </=50%) after suspected cardioembolic stroke.

Explore related subjects

Atrial FibrillationEchocardiography, TransesophagealEmbolismFemaleHumansMagnetic Resonance AngiographyMaleMyocardial IschemiaRisk FactorsStroke VolumeThrombosisVentricular Dysfunction, LeftJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hyung-Min Kwon, Jong-Ho Park, Jeong-Min Kim, Byung-Woo Yoon. 2006-10-20. Mild left ventricular dysfunction is associated with thrombogenicity in cardioembolic stroke.. https://doi.org/10.1159/000096489

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

RR-interval-based atrial fibrillation detection and burden estimation: cross-dataset validation and calibration-aware probability analysis.

Objective.Atrial fibrillation (AF) burden has become an increasingly important endpoint in long-duration rhythm monitoring, but reliable burden estimation requires more than accurate AF detection alone. In particular, when burden is derived by aggregating predicted AF probabilities over time, probability calibration may directly affect burden validity under external dataset shift.Approach.This study developed an interpretable-interval feature model for AF detection and evaluated it using record-wise cross-validation on a development cohort and independent cross-dataset external validation on public Holter electrocardiographic databases. Window-level performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve (PR-AUC), Brier score, expected calibration error (ECE), and calibration intercept and calibration slope. Recording-level AF burden was estimated using both probability-based and hard-label aggregation and evaluated using mean absolute error (MAE) and agreement analyses.Main results.The model showed high discrimination in both development and external evaluation, with external ROC-AUC ofand PR-AUC of. However, external calibration deteriorated despite preserved ranking performance, with Brier score of, ECE(15) of, calibration intercept of, and calibration slope of. In the external cohort, probability-based burden estimation preserved strong association with reference burden but showed weaker raw agreement than hard-label aggregation, with MAE ofversus, consistent with systematic probability underprediction. Repeated external recalibration across record-level splits substantially improved probability quality and probability-based burden estimation. Median probability-burden MAE decreased fromwithout recalibration toafter Platt recalibration andafter isotonic recalibration, while median ECE(15) decreased fromtoand, respectively.Significance.These findings indicate that-interval-based AF detection maintained strong ranking performance in the tested external cohort, but probability calibration should be evaluated explicitly when predicted probabilities are aggregated into AF-burden estimates.

Atrial Fibrillation↗
PubMed2026

Research on identification of key genes and immune-metabolic mechanisms in atrial fibrillation through integrated multi-cohort transcriptomic analysis and machine learning.

This study aimed to integrate multiple datasets for the identification of atrial fibrillation (AF)-related differentially expressed genes (DEGs), analyze their underlying mechanisms through functional enrichment and machine learning, construct diagnostic models, and explore immune-metabolic interactions to provide novel biomarkers and theoretical foundations. Gene expression datasets were integrated and normalized, with batch effects removed using principal component analysis. Differential expression analysis, functional enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways), and machine learning-based feature gene selection and model construction were performed. Shapley additive explanations analysis was utilized to interpret the constructed models, while gene set enrichment analysis, gene set variation analysis, and immune cell infiltration analysis were conducted to investigate the associations between feature genes and immune infiltration. After integrating and normalizing gene expression data and eliminating batch effects via principal component analysis, 6 DEGs were identified, including 4 upregulated and 2 down-regulated ones. Functional enrichment analysis showed these DEGs were significantly enriched in neuro-related biological processes and pathways, indicating their key roles in AF pathogenesis. Five key feature genes were selected using LASSO, random forest, and support vector machine-recursive feature elimination algorithms. They had significant expression differences between the AF and control groups (P&#x2005;<&#x2005;.001) and were located on distinct chromosomes. The constructed random forest and support vector machine models performed excellently (area under the curve&#x2005;&#x2265;&#x2005;0.85). Shapley additive explanations analysis revealed TNNI1 contributed most to model prediction, with its expression significantly positively correlated with immune cell infiltration. Gene set enrichment analysis and gene set variation analysis analyses further showed feature genes participated in AF pathogenesis by regulating immune modulation, metabolic pathways, and autophagy. Immune cell infiltration analysis found altered proportions of T-cell subsets and M0 macrophages in the AF group, along with complex links between feature gene expression and immune cell function. This study systematically elucidated the unique gene expression patterns and key regulatory pathways associated with AF, clarifying the crucial roles of feature genes in immune regulation, metabolic imbalance, and cellular dysfunction. These findings provide a theoretical basis and potential therapeutic targets for understanding AF pathogenesis and developing targeted treatment strategies.

Atrial Fibrillation↗
PubMed2025

Horse model of spontaneous atrial fibrillation share proteomic changes with humans.

Horses and humans are among the few mammals susceptible to spontaneous atrial fibrillation (AF), both suffering from high recurrence rates after treatment. Treatment resistance is often attributed to progressive atrial remodeling, but current treatment options fail to effectively address this aspect. Here, we introduce a novel horse model of spontaneous AF to investigate the biological pathway changes in early stages of the disease. Through data-independent acquisition mass spectrometry on biopsies from the right and left atrium and left ventricular chamber of horses with early-stage persistent AF (n&#x2009;=&#x2009;8) and controls (n&#x2009;=&#x2009;8), we identify several differentially regulated proteins across all three chambers. Pathway enrichment analyses and histological stainings highlight a significant role of atrial extracellular matrix (ECM) remodeling in early AF. Other key proteomic changes relate to metabolism, contractility, and protein-folding, and overlap with findings from publicly available human datasets. Our results demonstrate that horses and humans share several AF-related proteomic changes, providing translational insights into the early atrial remodeling processes that are likely to contribute to treatment resistance. These protein-level changes could serve as biomarkers or pharmacological targets for preventing AF-associated atrial remodeling and improve treatment outcomes across species.

Atrial Fibrillation↗