Background: Nurse-led disease management programs (DMPs) decrease readmission after acute decompensated heart failure (HF). We sought whether readmissions could be further reduced by lung ultrasound (LUS)-guided decongestion before discharge and during DMP.
Methods And Results: Of 290 patients hospitalized with acute decompensated HF, 122 at high risk for readmission or mortality were randomized to receive usual care (UC) (n = 64) or UC plus intervention (DMP-Plus) (n = 58), comprising LUS-guided management before discharge and during at-home follow-up.
Background: Residual congestion detected using handheld ultrasound may be associated with increased risk of readmission and death after hospitalization for acute decompensated heart failure (ADHF). However, effective application necessitates routine use by nonexperts delivering clinical care.
Objectives: The objective of this study was to determine the ability of heart failure (HF) nurses to deliver a predischarge lung and inferior vena cava (IVC) assessment (LUICA) to predict 90-day outcomes.
Aims: Effective and efficient education and patient engagement are fundamental to improve health outcomes in heart failure (HF). The use of artificial intelligence (AI) to enable more effective delivery of education is becoming more widespread for a range of chronic conditions. We sought to determine whether an avatar-based HF-app could improve outcomes by enhancing HF knowledge and improving patient quality of life and self-care behaviour.
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