Cardiovascular digital twin personalization using arterial blood pressure-driven optimization of a Windkessel-baroreflex model.

Authors

Rahmafatin Nurul Izza, Frederique Jos Vanheusden, Fauzan Syarif Nursyafi, Ki Moo Lim

Published in

Biomedical engineering letters. Volume 16. Issue 5. Pages 1261-1271. Epub May 20, 2026.

Abstract

Cardiovascular digital twins have emerged as powerful tools for modeling patient-specific cardiovascular physiology and supporting clinical decision-making. However, their practical adoption remains limited due to reliance on invasive or imaging-based data and computationally intensive personalization procedures. This study presents a cardiovascular digital twin personalization framework that integrates circulation system hemodynamic parameter optimization with arterial baroreflex gain calibration using only non-invasive arterial blood pressure measurements. A twelve-compartment Windkessel-baroreflex model was personalized in two steps. First, left ventricular end-systolic elastance and global vascular resistance and compliance scaling were optimized from a single resting arterial pressure waveform. After this, arterial baroreflex gains for total peripheral resistance and venous unstressed volume were tuned from beat-by-beat responses during head-up tilt and subject characteristics. In 10 PhysioNet subjects, the personalized models reproduced resting systolic, diastolic, and mean arterial pressures with absolute errors below 5 mmHg. The model also reproduced the main features of heart-rate and arterial-pressure responses during rapid and slow tilt, with better quantitative agreement for heart rate than for mean arterial pressure. These findings support the feasibility of constructing personalized cardiovascular digital twins from non-invasive arterial pressure recordings, while indicating the need for further refinement and broader validation before reliable subject-specific pressure prediction can be claimed.
The online version contains supplementary material available at https://doi.org/10.1007/s13534-026-00584-x.

PMID:
42801034
Bibliographic data and abstract were imported from PubMed on 27 Sep 2026.

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