# Dynamic models of the cardiovascular system capturing years, rather than heartbeats

> **NIH NIH DP1** · DUKE UNIVERSITY · 2024 · $1,127,000

## Abstract

Predicting how a particular patient's vascular system with respond to different treatment or
stimuli and adapt over long periods of time remains a grand challenge in precision medicine.
The lack of real-time turn around critically limits our ability to search a wide treatment space to
identify optimal intervention plans based on long-term, personalized predictions. Moreover, it
prevents real-time monitoring of a patient's hemodynamics based on streaming, dynamic data
such as that acquired from wearables. By moving from simulations that can capture only several
heartbeats to modeling months or even years, we shift the utilization of patient-specific digital
twins to provide on-demand tracking of a patient's hemodynamic state. Such data would
improve screening for cardiovascular disease, improved monitoring, and finally, inform
treatment planning by enabling prediction of longterm flow effects currently not attainable. The
major objective of this proposal is to develop and apply a methodology coupling physics-based
simulations with machine learning that, combined with wearable sensors, provides real-time,
personalized predictions of 3D, complex hemodynamic patterns over months to years. A better
understanding of how a patient's circulatory system and underlying hemodynamics responds
under different physiological states over time is of broad relevance to treating a wide range of
vascular diseases.

## Key facts

- **NIH application ID:** 10899484
- **Project number:** 5DP1AG082343-03
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Amanda E Randles
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,127,000
- **Award type:** 5
- **Project period:** 2022-09-30 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899484

## Citation

> US National Institutes of Health, RePORTER application 10899484, Dynamic models of the cardiovascular system capturing years, rather than heartbeats (5DP1AG082343-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10899484. Licensed CC0.

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