Yihui Kong

Evidence on the association between cardiovascular health (CVH) and frailty remains limited, and the role of serum 25-hydroxyvitamin D (25(OH)D) in this relationship is not fully understood. This study aimed to examine the association between frailty and CVH and to investigate the independent association of serum 25(OH)D and its potential interaction with frailty. This cross-sectional study used data from the National Health and Nutrition Examination Survey 2007 to 2018, including 8015 adults aged ≥20 years. Frailty was assessed using a frailty index, and CVH was evaluated based on the American Heart Association’s Life’s Essential 8 metrics. Multinomial logistic regression models accounting for the complex survey design were applied to assess associations. Frailty was highly prevalent in the study population. After multivariable adjustment, higher frailty levels were significantly associated with an increased risk of poor CVH in a graded manner. Serum 25(OH)D levels were independently and inversely associated with poor CVH. Stratified analyses showed that the association between frailty and CVH varied by sex and 25(OH)D status. However, no statistically significant multiplicative interaction between frailty and serum 25(OH)D levels was observed. Frailty was significantly associated with poorer CVH in US adults, demonstrating a clear dose–response relationship. Serum 25(OH)D levels were independently associated with CVH, whereas no statistically significant multiplicative interaction between frailty and serum 25(OH)D was observed. These findings highlight the importance of addressing multiple modifiable risk factors to improve CVH.

Background: This study developed a deep learning model to predict improvement of left ventricular ejection fraction in patients with heart failure. Methods: An internal database comprising clinical, laboratory and echocardiographic features obtained from the First Affiliated Hospital of Harbin Medical University was used to construct and train the deep learning model. Results: A total of 422 cases were included in this study. 122 (28.9%) were patients with HFimpEF, 300 (71.0%) were patients with HFrEF. Multivariable analyses showed that smaller baseline left atrial anterior-posterior diameter (LAD) and left ventricular end-diastolic diameter (LVEDD), higher baseline interventricular septal thickness at diastole (IVSD) and levels of prealbumin were the independent clinical predictors of LVEF improvement. Deep learning model demonstrated an overall predict accuracy of 96% in the validation set and 89% in the training set. Conclusions: Independent predictors of LVEF improvement were smaller baseline LVEDD, LAD, higher baseline IVSD and baseline levels of prealbumin. Our deep learning model had shown acceptable performance in predicting improvement of left ventricular ejection fraction in patients with heart failure. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT06070506 .