Novel Technologies to Improve Echocardiographic Estimates of Left Ventricular Filling Pressure in Heart Failure Combined With Atrial Fibrillation

Study Summary

Heart failure and atrial fibrillation are two of the most common heart diseases globally. Nearly half of all patients with heart failure also have atrial fibrillation. When heart failure and atrial fibrillation occur together, the risk of hospitalization and premature death increases significantly. However, there is a lack of reliable tools to assess how severely the heart is affected in these patients. This makes it difficult both to establish the correct diagnosis, tailor treatment, and predict who is at greatest risk of hospital admission or death from the disease. One of the most important targets in heart failure is the filling pressure in the left ventricle. When this pressure is high, it means that the heart has difficulty receiving blood, leading to shortness of breath and fluid retention in the body. Today, filling pressure is usually estimated using ultrasound (echocardiography), but the available methods are primarily developed for patients without atrial fibrillation. In patients with both heart failure and atrial fibrillation, the measurements are so uncertain that they cannot be used as a reliable basis for clinical decision-making. In this study, entitled Heart Failure combined with Atrial Fibrillation (HFcAF), the investigators will test new ultrasound methods that combine novel measures of cardiac chamber function with established techniques. Artificial intelligence will be used to identify the most useful combinations of parameters, select cardiac cycles that are best suited for analysis in atrial fibrillation, and automate and optimize the measurements. This approach may provide both more accurate and faster assessments, while also making the methods easier to implement in clinical practice. The aim is to improve the estimation of filling pressure so that it becomes more precise also in patients with atrial fibrillation. The investigators will then examine whether these improved methods can be used to predict which patients are at highest risk o