Background: Takotsubo syndrome (TS) is a recently described cardiac syndrome whose pathogenesis is still unclear. We investigated the characteristics of coronary microcirculatory function in patients with TS through the analysis of the TIMI frame count (TFC) compared to normal subjects and with to subjects with microvascular angina (MA).
Methods: We enrolled 71 TS patients (F:M =69:2, mean age of 65.27±9.53 years), 70 controls (F:M =34:36, mean age of 56.63±13.5 years) and 71 patients with MA, (F:M =69:2, mean age of 65.9±9.2 years). The assessment of the microcirculation was carried out through the TFC.
Results: microcirculation was significantly altered in patients with TS compared with healthy controls [left anterior descending coronary artery (LAD) 25.16±6.91 17.30±3.76, P<0.001; circumflex artery (CX) 25.48±6.10 17.05±4.60, P<0.001; right coronary artery (RCA) 26.43±8.95 15.74±4.27, P<0.001, average TFC in TS 25.70±5.34 16,70±3.26, P<0.001). A TFC >20 frames was able to discriminate TS patients from controls with a specificity of 88.57% and sensitivity of 85.92% (AUC 0.927, P<0.0001). Microvascular dysfunction was diffuse in TS as well as in MA and slightly more severe in this last (mean TFC in MA 28.25±9.3 25.7±5.34 in TS, P<0.046).
Conclusions: Coronary microcirculation in TS patients is diffuse and milder compared to MA patients. Cut-off values >20 frames discriminate between patients with normal microcirculation and patients with TS.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5721058 | PMC |
| http://dx.doi.org/10.21037/jtd.2017.10.93 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.
PLoS One
January 2025
Institute for Physical Activity and Nutrition, Deakin University, Melbourne, VIC, Australia.
Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators.
View Article and Find Full Text PDFDeep learning model for identifying acute heart failure patients using electrocardiography in the emergency room.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.
Methods: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study.
Infective endocarditis caused by - a retrospective registry-based study.
Infect Dis (Lond)
January 2025
Division of Infection Medicine, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
Purpose: Infective endocarditis (IE) is diagnosed using the Duke criteria, which were updated in 2023. In the Duke-ISCVID 2023 criteria, was recognised as a typical IE pathogen. This study investigates the impact of this change and compares the clinical characteristics of IE to IE caused by other pathogens.
View Article and Find Full Text PDFRestrictive or Liberal Blood Transfusion in Patients with Myocardial Infarction and CKD.
J Am Soc Nephrol
January 2025
Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
Background: Chronic kidney disease (CKD) is associated with higher risk of myocardial infarction and anemia. Among patients with anemia and CKD who experience myocardial infarction, it remains uncertain if a liberal red blood cell transfusion threshold strategy (hemoglobin cutoff [Hgb] < 10 g/dL) is superior to a restrictive transfusion threshold (Hgb 7-8 g/dL) strategy.
Methods: Among the 3,504 patients enrolled in the Myocardial Ischemia and Transfusion (MINT) trial with non-missing creatinine, we compared baseline characteristics and 30-day and 6-month outcomes of patients without CKD (N = 1279), CKD with eGFR 30-60 mL/min/1.
Use of mHealth Technology for Improving Exercise Adherence in Patients With Heart Failure: Systematic Review.
J Med Internet Res
January 2025
College of Nursing, University of Nebraska Medical Center, Omaha, NE, United States.
Background: The known and established benefits of exercise in patients with heart failure (HF) are often hampered by low exercise adherence. Mobile health (mHealth) technology provides opportunities to overcome barriers to exercise adherence in this population.
Objective: This systematic review builds on prior research to (1) describe study characteristics of mHealth interventions for exercise adherence in HF including details of sample demographics, sample sizes, exercise programs, and theoretical frameworks; (2) summarize types of mHealth technology used to improve exercise adherence in patients with HF; (3) highlight how the term "adherence" was defined and how it was measured across mHealth studies and adherence achieved; and (4) highlight the effect of age, sex, race, New York Heart Association (NYHA) functional classification, and HF etiology (systolic vs diastolic) on exercise adherence.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!