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A Nomogram utilizing ECG P-wave parameters to predict recurrence risk following catheter ablation in paroxysmal atrial fibrillation.
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January 2025
Department of Cardiology, Fujian Medical University Union Hospital, Fujian Heart Medical Center, Fujian Institute of Coronary Heart Disease, Fujian Clinical Medical Research Center for Heart and Macrovascular Disease, Fuzhou, 350001, China.
Objective: The objective of this study is to assess the predictive utility of perioperative P-wave parameters in patients with paroxysmal atrial fibrillation (PAF) undergoing catheter ablation, and to develop a predictive model using these parameters.
Methods: A total of 213 patients with PAF undergoing catheter ablation were retrospectively analyzed. P-wave parameters were measured within 3 days preoperatively and on the day postoperatively to determine their predictive significance for postoperative PAF recurrence.
Association between triglyceride glucose index and adverse cardiovascular prognosis in patients with atrial fibrillation without diabetes: a retrospective cohort study.
Lipids Health Dis
January 2025
Department of Cardiology, West China Hospital, Sichuan University West China School of Medicine, 37 Guoxue Road, Chengdu, Sichuan, 610041, China.
Background: Atrial fibrillation (AF) is the most prevalent arrhythmia encountered in clinical practice. Triglyceride glucose index (Tyg), a convenient evaluation variable for insulin resistance, has shown associations with adverse cardiovascular outcomes. However, studies on the Tyg index's predictive value for adverse prognosis in patients with AF without diabetes are lacking.
View Article and Find Full Text PDFCachectic biomarkers as confounders behind the obesity paradox in patients with acute decompensated heart failure.
Int J Obes (Lond)
January 2025
Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan.
Background: Obesity is a risk factor for heart failure (HF) development but is associated with a lower incidence of mortality in HF patients. This obesity paradox may be confounded by unrecognized comorbidities, including cachexia.
Methods: A retrospective assessment was conducted using data from a prospectively recruiting multicenter registry, which included consecutive acute heart failure patients.
Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.
Sci Rep
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.
Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of cardiovascular disease and mental health problems. Early detection and intervention are crucial but often hindered by complex diagnostic requirements. This study aims to develop a predictive model using NHANES data, excluding biochemical indicators, to provide a simple, cost-effective tool for large-scale, non-medical screening and early prevention of adolescent MetS.
View Article and Find Full Text PDFIdentifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.
J Prev Alzheimers Dis
February 2025
Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. Electronic address:
Background: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is challenging by their high dimensionality and inherent correlations.
Methods: Our study analyzed 1128 plasma proteins, measured by the SOMAscan platform, from 858 participants 55 years and older (mean age 63 years, 52.
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