ArticleBiomechanics and modeling in mechanobiology2019
A data-driven model to study utero-ovarian blood flow physiology during pregnancy.
Article in Biomechanics and modeling in mechanobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Pooled it
- Influence of arteries, vein and placenta interactions on the blood flow in the human umbilical cord.PloS one · 2026Article
- Modelling maternal cardiovascular adaptation to pregnancy: a scoping review.BMC pregnancy and childbirth · 2025Article
- Integrating traditional Chinese pulse diagnosis with machine learning: novel approaches for pregnancy and coronary heart disease identification.Scientific reports · 2025Article
- Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models.Scientific reports · 2024Article
- Adnexal Masses in Pregnancy: A Single-Centre Prospective Observational Cohort Study.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet.American journal of physiology. Heart and circulatory physiology · 2023Review
- Ventricular wall stress and wall shear stress homeostasis predicts cardiac remodeling during pregnancy: A modeling study.International journal for numerical methods in biomedical engineering · 2022Article
- Personalising cardiovascular network models in pregnancy: A two-tiered parameter estimation approach.International journal for numerical methods in biomedical engineering · 2021Article
- A framework for incorporating 3D hyperelastic vascular wall models in 1D blood flow simulations.Biomechanics and modeling in mechanobiology · 2021Article
- Case of placental insufficiency and premature delivery in a Fontan pregnancy: physiological insights and considerations on risk stratification.Open heart · 2021Article
- Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2020Article
- Computational instantaneous wave-free ratio (IFR) for patient-specific coronary artery stenoses using 1D network models.International journal for numerical methods in biomedical engineering · 2019Article
- Non-invasive coronary CT angiography-derived fractional flow reserve: A benchmark study comparing the diagnostic performance of four different computational methodologies.International journal for numerical methods in biomedical engineering · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
In this paper, we describe a mathematical model of the cardiovascular system in human pregnancy. An automated, closed-loop 1D-0D modelling framework was developed, and we demonstrate its efficacy in (1) reproducing measured multi-variate cardiovascular variables (pulse pressure, total peripheral resistance and cardiac output) and (2) providing automated estimates of variables that have not been measured (uterine arterial and venous blood flow, pulse wave velocity, pulsatility index). This is the first model capable of estimating volumetric blood flow to the uterus via the utero-ovarian communicating arteries. It is also the first model capable of capturing wave propagation phenomena in the utero-ovarian circulation, which are important for the accurate estimation of arterial stiffness in contemporary obstetric practice. The model will provide a basis for future studies aiming to elucidate the physiological mechanisms underlying the dynamic properties (changing shapes) of vascular flow waveforms that are observed with advancing gestation. This in turn will facilitate the development of methods for the earlier detection of pathologies that have an influence on vascular structure and behaviour.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.