Key result
AI-ECG models identify elevated mean pulmonary capillary wedge pressure non-invasively.
Why the study?
Elevated mean pulmonary capillary wedge pressure is a key marker of heart failure severity but requires invasive right heart catheterization, limiting widespread use.
Observational
Answers are grounded in this paper and cite their sources.
ECG-AI may enable non-invasive mPCWP estimation; leaves open prospective validation before clinical adoption.
Elevated mean pulmonary capillary wedge pressure (mPCWP) is a key hemodynamic marker of heart failure (HF) severity, but requires invasive right heart catheterization (RHC), limiting widespread use. The electrocardiogram (ECG) is ubiquitous, inexpensive, and may contain latent hemodynamic information detectable by AI. We aim to develop and validate ECG-based machine learning (ML) and deep learning models for non-invasive classification of elevated mPCWP (≥18 mmHg), using invasive RHC as the gold standard reference.
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Sandesara et al. (2026) conducted an observational in Heart Failure. Electrocardiographic artificial intelligence (ECG-AI) vs. Invasive right heart catheterization (RHC) was evaluated on Classification of elevated mean pulmonary capillary wedge pressure (≥18 mmHg). The abstract outlines the aim to develop and validate ECG-based artificial intelligence models for the non-invasive classification of elevated mean pulmonary capillary wedge pressure.
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