RESUMO
BACKGROUND: The efficiency of the target versus sub-target dose of renin-angiotensin system inhibitors (RASIs) in elderly patients with heart failure (HF) with reduced ejection fraction (HErEF) remains unclear. METHODS: PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched from database inception through March 2022 for randomized controlled trials (RCTs) and observational studies considering the effect of the target versus sub-target dose of RASIs on survival in elderly patients (≥ 60 years) with HErEF. The primary outcome was all-cause mortality. The secondary outcomes were cardiac mortality, HF hospitalization, and the composite endpoint of mortality or HF hospitalization. A meta-analysis was conducted to generate combined hazard ratio (HR) and 95% CI. RESULTS: Seven studies (two RCTs and five observational studies) enrolling 16,634 patients were included. A pooled analysis suggested that the target versus sub-target dose of RASIs led to lower rates of all-cause mortality (HR = 0.92, 95% CI: 0.87-0.98, I2 = 21%) and cardiac mortality (HR = 0.93, 95% CI: 0.85-1.00, I2 = 15%) but not reduced rates of HF hospitalization (HR = 0.94, 95% CI: 0.88-1.01, I2 = 0) and the composite endpoint (HR = 1.03, 95% CI: 0.91-1.15, I2 = 51%). However, the target dose of RASIs was associated with a similar primary outcome (HR = 0.85, 95% CI: 0.64-1.14, I2 = 0) in a subgroup of very elderly patients > 75 years of age. CONCLUSIONS: Our analysis suggests that the target dose of RASIs has a better survival benefit in elderly patients with HFrEF compared to the sub-target dose of RASIs. However, the sub-target dose of RASIs is associated with a similar mortality rate in very elderly patients > 75 years of age. Future high-quality and adequately powered RCTs are warranted.
RESUMO
Segmentation of the left ventricular (LV) myocardium in 2-D echocardiography is essential for clinical decision making, especially in geometry measurement and index computation. However, segmenting the myocardium is a time-consuming process and challenging due to the fuzzy boundary caused by the low image quality. The ground-truth label is employed as pixel-level class associations or shape regulation in segmentation, which works limit for effective feature enhancement for 2-D echocardiography. We propose a training strategy named multiconstrained aggregate learning (referred to as MCAL), which leverages anatomical knowledge learned through ground-truth labels to infer segmented parts and discriminate boundary pixels. The new framework encourages the model to focus on the features in accordance with the learned anatomical representations, and the training objectives incorporate a boundary distance transform weight (BDTW) to enforce a higher weight value on the boundary region, which helps to improve the segmentation accuracy. The proposed method is built as an end-to-end framework with a top-down, bottom-up architecture with skip convolution fusion blocks and carried out on two datasets (our dataset and the public CAMUS dataset). The comparison study shows that the proposed network outperforms the other segmentation baseline models, indicating that our method is beneficial for boundary pixels discrimination in segmentation.
