Background: Heart failure (HF) and atrial fibrillation (AF) represent conditions that commonly coexist. The impact of AF in HF has yet to be well studied in Latin America. This study aimed to characterize the sociodemographic and clinical features, along with patients' outcomes with AF and HF from the Colombian Heart Failure Registry (RECOLFACA).
Methods: Patients with ambulatory HF and AF were included in RECOLFACA, mainly with persistent or permanent AF. A 6-month follow-up was performed. Primary outcome was all-cause mortality. To assess the impact of AF on mortality, we used a logistic regression model. A P value of < 0.05 was considered significant. All statistical tests were two-tailed.
Results: Of 2,528 patients with HF in the registry, 2,514 records included information regarding AF diagnosis. Five hundred sixty (22.3%) were in AF (mean age 73 ± 11, 56% men), while 1,954 had no AF (mean age 66 ± 14 years, 58% men). Patients with AF were significantly older and had a different profile of comorbidities and implanted devices compared to non-AF patients. Moreover, AF diagnosis was associated with lower quality of life score (EuroQol-5D), mainly in mobility, personal care, and daily activity. AF was prevalent in patients with preserved ejection fraction (EF), while no significant differences in N-terminal prohormone of brain natriuretic peptide (NT-proBNP) levels were observed. Although higher mortality was observed in the AF group compared to individuals without AF (8.9% vs. 6.1%, respectively; P = 0.016), this association lost statistical significance after adjusting by age in a multivariate regression model (odds ratio (OR): 1.35; 95% confidence interval (CI): 0.95 - 1.92).
Conclusions: AF is more prevalent in HF patients with higher EF, lower quality of life and different clinical profiles. Similar HF severity and non-independent association with mortality were observed in our cohort. These results emphasize the need for an improved understanding of the AF and HF coexistence phenomenon.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10923258 | PMC |
| http://dx.doi.org/10.14740/cr1589 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comprehensive analysis of scRNA-seq and bulk RNA-seq reveals the non-cardiomyocytes heterogeneity and novel cell populations in dilated cardiomyopathy.
J Transl Med
January 2025
State Key Laboratory of Cardiovascular Diseases and Medical Innovation Center, School of Medicine, Shanghai East Hospital, Tongji University, Shanghai, 200120, China.
Background: Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Infiltration and alterations in non-cardiomyocytes of the human heart involve crucially in the occurrence of DCM and associated immunotherapeutic approaches.
Methods: We constructed a single-cell transcriptional atlas of DCM and normal patients.
Letter to the Editor regarding 'triglyceride glucose index: a significant prognostic marker of heart failure in patients with chronic kidney disease'.
Ren Fail
December 2025
Department of Endocrinology, Institute of Geriatric Medicine, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor.
Commun Med (Lond)
January 2025
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.
Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.
Development and validation of a nomogram to predict survival in septic patients with heart failure in the intensive care unit.
Sci Rep
January 2025
China Academy of Chinese Medical Sciences, Beijing, China.
Heart failure is a common complication in patients with sepsis, and individuals who experience both sepsis and heart failure are at a heightened risk for adverse outcomes. This study aims to develop an effective nomogram model to predict the 7-day, 15-day, and 30-day survival probabilities of septic patients with heart failure in the intensive care unit (ICU). This study extracted the pertinent clinical data of septic patients with heart failure from the Critical Medical Information Mart for Intensive Care (MIMIC-IV) database.
View Article and Find Full Text PDFA novel RFE-GRU model for diabetes classification using PIMA Indian dataset.
Sci Rep
January 2025
Department of Computer Science, Faculty of Computers and Information, Suez University, P. O. Box 43221, Suez, Egypt.
Diabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal failure, and heart attack. Diabetes requires the most machine learning help to diagnose diabetes illness at an early stage, as it cannot be treated and adds significant complications to our health-care system.
View Article and Find Full Text PDFWant 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!