Patient-specific hemodynamic simulation for left ventricular assist device via non-invasive monitoring based on lumped parameter model and hierarchical neural network.

INTRODUCTION

Left ventricular assist devices (LVADs) are widely used in advanced heart failure, but require accurate hemodynamic assessment for optimal management. Current invasive methods such as right-heart catheterisation (RHC) are limited in routine use, highlighting the need for non-invasive alternatives.

METHODS

A non-invasive framework combining a lumped parameter model (LPM) with a hierarchical neural network (CLPM-Net) was developed to estimate patient-specific hemodynamic parameters from echocardiography and blood pressure. Model identifiability analysis was performed to select key parameters. The model was trained on synthetic data and validated with clinical cases.

RESULTS

The proposed method achieved accurate parameter estimation with errors below 10% (RMSE). Simulated hemodynamic indicators showed strong agreement with ground truth (nMED < 1%). Clinical validation demonstrated close consistency with invasive measurements.

DISCUSSION: This framework enables non-invasive, patient-specific hemodynamic assessment for LVAD management. It shows potential as an alternative to invasive monitoring, though further large-scale clinical validation is required.