Background: Early recognition of volume overload is essential for heart failure patients. Volume overload can often be easily treated if caught early but causes significant morbidity if unrecognized and allowed to progress. Intravascular volume status can be assessed by ultrasound-based estimation of right atrial pressure (RAP), but the availability of this diagnostic modality is limited by the need for experienced physicians to accurately interpret these scans.
Objectives: We sought to evaluate whether machine learning can accurately estimate echocardiogram-measured RAP.
Methods: We developed fully automated deep learning models for identifying inferior vena cava scans with rapid inspiration in echocardiogram studies and estimating RAP from those scans. The RAP estimation model was trained and evaluated using 15,828 ultrasound videos of the inferior vena cava and coupled cardiologist-assessed RAP estimates as well as 319 RAP measurements from right heart catheterization.
Results: Our model agreed with cardiologist estimates 80.3% of the time (area under the receiver-operating characteristic of 0.844) in a test data set, at the upper end of interoperator agreement rates found in the literature of 70 to 75%. Our model's RAP estimates were statistically indistinguishable from cardiologists' ultrasound-based RAP estimates (P = 0.98) when compared against the gold standard of right heart catheterization RAP measurements in a subset of patients. Our model also generalized well to an external data set of echocardiograms from a different institution (area under the receiver-operating characteristic of 0.854 compared to cardiologist RAP estimates).
Conclusions: Machine learning is capable of accurately and robustly interpreting RAP from echocardiogram videos. This algorithm could be used to perform automated assessments of intravascular volume status.
Keywords: artificial intelligence; deep learning; echocardiography; heart failure; vascular congestion.
© 2024 The Authors.