Beta-blockers and 1-year clinical outcomes in hospitalized heart failure patients with atrial fibrillation.

OBJECTIVE: To assess the association between beta-blockers and 1-year clinical outcomes in heart failure (HF) patients with atrial fibrillation (AF), and further explore this association that differs by left ventricular ejection fraction (LVEF) level. METHODS: We enrolled hospitalized HF patients with AF from China Patient-centered Evaluative Assessment of Cardiac Events Prospective Heart Failure Study. COX proportional hazard regression models were employed to calculate hazard ratio of beta-blockers. The primary outcome was all-cause death. RESULTS: Among 1762 HF patients with AF (756 women [41.4%]), 1041 (56%) received beta-blockers at discharge and 1272 (72.2%) had an LVEF > 40%. During one year follow up, all-cause death occurred in 305 (17.3%), cardiovascular death occurred in 203 patients (11.5%), and rehospitalizations for HF occurred in 622 patients (35.2%). After adjusting for demographic characteristics, social economic status, smoking status, medical history, anthropometric characteristics, and medications used at discharge, the use of beta-blockers at discharge was not associated with all-cause death [hazard ratio (HR): 0.86; 95% Confidence Interval (CI): 0.65-1.12; P = 0.256], cardiovascular death (HR: 0.76, 95% CI: 0.52-1.11; P = 0.160), or the composite outcome of all-cause death and HF rehospitalization (HR: 0.97, 95% CI: 0.82-1.14; P = 0.687) in the entire cohort. There were no significant interactions between use of beta-blockers at discharge and LVEF with respect to all-cause death, cardiovascular death, or composite outcome. In the adjusted models, the use of beta-blockers at discharge was not associated with all-cause death, cardiovascular death, or composite outcome across the different levels of LVEF: reduced (< 40%), mid-range (40%-49%), or preserved LVEF (≥ 50%). CONCLUSION: Among HF patients with AF, the use of beta-blockers at discharge was not associated with 1-year clinical outcomes, regardless of LVEF.

Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction.

The present study aimed to identify genes associated with increased risk of myocardial infarction (MI) and construct an early diagnosis model based on support vector machine (SVM) learning. The gene expression profile data of GSE34198, containing 97 human blood samples including 49 patients with MI and 48 healthy individuals, were obtained from the Gene Expression Omnibus database. Differentially expressed gene (DEG) screening, DEG enrichment analysis, protein­protein interaction (PPI) network investigation and clustering analysis were performed. The feature genes were identified using the neighboring score algorithm. Furthermore, a recursive feature elimination (RFE) algorithm was employed to screen risk factors among feature genes. The SVM prediction model was constructed and validated using the dataset GSE61144. A total of 1,207 DEGs (724 downregulated, 483 upregulated) between the two groups were identified. PPI analysis investigated 1,083 DEGs and 46,363 edges. In total, 87 genes were selected as candidate genes, and were primarily enriched in functions including 'G­protein coupled receptor signaling' or pathways such as 'focal adhesion'. Furthermore, 15 genes with a high RFE score were selected to construct an SVM prediction model. The model's average accuracy was 86%. Data set verification showed that the predictive precision reached 0.92. High expression of the genes vascular endothelial growth factor A, A­kinase anchoring protein 12 and olfactory receptor 8D2 were potential risk factors for MI. The SVM early diagnosis model constructed by candidate genes could not only predict early MI, but also provide risk probability according to the severity of MI.