Development and Independent Validation of a Machine Learning-Based Non-Invasive Venous Waveform Analysis for Heart Failure (NIVA_HF) Device to Estimate Pulmonary Capillary Wedge Pressure

The authors aimed to develop and validate a machine learning-based device, NIVA_HF, for non-invasively estimating pulmonary capillary wedge pressure (PCWP) from peripheral venous waveforms in heart failure patients. In a multicenter study, they found that the NIVA Score showed a mean bias of -1.06 mmHg compared to invasively measured PCWP, indicating good agreement and high sensitivity for detecting elevated filling pressures. This suggests that NIVA_HF could serve as a valuable non-invasive tool for monitoring congestion in heart failure management.