Objectives
To investigate the clinical characteristics of elderly inpatients with heart failure (HF) with preserved ejection fractionand and atrial fibrillation (AF).
Methods
The elderly inpatients (age≥60 years) with HF and AF hospitalized in Fujian Medical University Union Hospital from January 1, 2014 to January 1, 2017 were enrolled and divided into two subgroups based on left ventricular ejection fraction (LVEF), heart failure with preserved ejection fraction and AF (HFpEF-AF, LVEF≥40%)and heart failure with reduced ejection fraction and AF (HFrEF-AF, LVEF<40%). Clinical characteristics between these two groups were compared.
Results
A total of 696 elderly inpatients with HF and AF were enrolled, of which 545 cases were HFpEF-AF (78.3%) and other 151 were HFrEF-AF. Compared with HFrEF-AF, patients with HFpEF-AF were older with higher body mass index, more women and higher proportion of hypertension (all P 0.05). The in-hospital mortality of HFpEF-AF patients was higher than that of HFrEF-AF patients without significant difference (6.4% vs. 4.0%, P=0.258).
Conclusions
The majority of elderly inpatients with HF and AF was HFpEF-AF. Compared with HFrEF-AF patients, HFpEF-AF patients were older, fatter, with more women and higher proportion of hypertension. The risk of stroke and in-hospitality mortality was higher in patients with HFpEF-AF compared to HFrEF-AF despite without significant difference.
Key words:
Heart failure; Left ventricular ejection fraction; Atrial fibrillation; Elderly; Stroke
According to the question of the traditional multi-level association rules mining in large data mining in low efficiency and accuracy, based on clustering classification multi-level association rule mining is proposed. The method is combined with the concept of hierarchical concept, the data of the generalization sets processing, and uses SOFM neural network generalization into the database after the transaction, by way of introducing an internal threshold so no need to set the minimum support threshold, to generate the local frequent item sets as global candidates item sets to generate global frequent item sets, thereby enhancing the efficiency of multi-level association rules and accuracy. And by simulating the case shows that the method can not only efficient mining single-layer and cross-layer association rules, but also the association rules is new, easy to understand and meaningful.
