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Frontiers in physiology · 2026

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

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Lyu Jiayi, Liu Xiaoyu, Lin Zhihong, Ji Baohua, Gao Qi, Shu Qiang, Fan Xiangming

Аннотация

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.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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