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Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models

Raoul van Loon Orcid Logo, Claudia Popp, Jason Carson Orcid Logo, Alexander Drysdale, Hari Arora Orcid Logo, Edward D. Johnstone, Jenny E. Myers

Scientific Reports

Swansea University Authors: Raoul van Loon Orcid Logo, Claudia Popp, Jason Carson Orcid Logo, Alexander Drysdale, Hari Arora Orcid Logo

Abstract

Computational models can be at the basis of new powerful technologies for studying and classifying disorders like pre-eclampsia, where it is difficult to distinguish pre-eclamptic patients from non-pre-eclamptic based on pressure when patients have a track record of hypertension. Computational model...

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URI: https://cronfa.swan.ac.uk/Record/cronfa67777
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spelling v2 67777 2024-09-24 Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models 880b30f90841a022f1e5bac32fb12193 0000-0003-3581-5827 Raoul van Loon Raoul van Loon true false 1e879f6786330055618b55abbef1569c Claudia Popp Claudia Popp true false c1f2c28fbe6a41c5134b45abde5abb93 0000-0001-6634-9123 Jason Carson Jason Carson true false 59f357e91ed91f03597ac28978e6bc30 Alexander Drysdale Alexander Drysdale true false ed7371c768e9746008a6807f9f7a1555 0000-0002-9790-0907 Hari Arora Hari Arora true false 2024-09-24 EAAS Computational models can be at the basis of new powerful technologies for studying and classifying disorders like pre-eclampsia, where it is difficult to distinguish pre-eclamptic patients from non-pre-eclamptic based on pressure when patients have a track record of hypertension. Computational models now enable a detailed analysis of how pregnancy affects the cardiovascular system. Objective – to develop new non-invasive biomarkers that can aid the classification of pre-eclampsia through the integration of six different measured non-invasive cardiovascular signals. Methods – datasets of 21 pregnant women (no early onset pre-eclampsia, n=12; early onset pre-eclampsia, n=9) were used to create personalised cardiovascular models through computational modelling resulting in predictions of blood pressure and flow waveforms in all major and minor vessels of the utero-ovarian system. Main results – the analysis performed revealed that the new predictors PPI (pressure pulsatility index) and RI (resistance index) calculated in arcuate and radial/spiral arteries are able to differentiate between the 2 groups of women (t-test scores of p < .001) better than PI (pulsatility index) and RI (Doppler calculated in the uterine artery) for both supervised and unsupervised classification. Conclusion – two novel high-performing biomarkers for the classification of pre-eclampsia have been identified based on blood velocity and pressure predictions in the smaller placental vasculatures where non-invasive measurements are not feasible. Journal Article Scientific Reports 0 0 0 0001-01-01 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University 2024-09-26T14:57:37.1223118 2024-09-24T15:13:51.3518863 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Raoul van Loon 0000-0003-3581-5827 1 Claudia Popp 2 Jason Carson 0000-0001-6634-9123 3 Alexander Drysdale 4 Hari Arora 0000-0002-9790-0907 5 Edward D. Johnstone 6 Jenny E. Myers 7
title Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
spellingShingle Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
Raoul van Loon
Claudia Popp
Jason Carson
Alexander Drysdale
Hari Arora
title_short Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
title_full Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
title_fullStr Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
title_full_unstemmed Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
title_sort Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
author_id_str_mv 880b30f90841a022f1e5bac32fb12193
1e879f6786330055618b55abbef1569c
c1f2c28fbe6a41c5134b45abde5abb93
59f357e91ed91f03597ac28978e6bc30
ed7371c768e9746008a6807f9f7a1555
author_id_fullname_str_mv 880b30f90841a022f1e5bac32fb12193_***_Raoul van Loon
1e879f6786330055618b55abbef1569c_***_Claudia Popp
c1f2c28fbe6a41c5134b45abde5abb93_***_Jason Carson
59f357e91ed91f03597ac28978e6bc30_***_Alexander Drysdale
ed7371c768e9746008a6807f9f7a1555_***_Hari Arora
author Raoul van Loon
Claudia Popp
Jason Carson
Alexander Drysdale
Hari Arora
author2 Raoul van Loon
Claudia Popp
Jason Carson
Alexander Drysdale
Hari Arora
Edward D. Johnstone
Jenny E. Myers
format Journal article
container_title Scientific Reports
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 0
active_str 0
description Computational models can be at the basis of new powerful technologies for studying and classifying disorders like pre-eclampsia, where it is difficult to distinguish pre-eclamptic patients from non-pre-eclamptic based on pressure when patients have a track record of hypertension. Computational models now enable a detailed analysis of how pregnancy affects the cardiovascular system. Objective – to develop new non-invasive biomarkers that can aid the classification of pre-eclampsia through the integration of six different measured non-invasive cardiovascular signals. Methods – datasets of 21 pregnant women (no early onset pre-eclampsia, n=12; early onset pre-eclampsia, n=9) were used to create personalised cardiovascular models through computational modelling resulting in predictions of blood pressure and flow waveforms in all major and minor vessels of the utero-ovarian system. Main results – the analysis performed revealed that the new predictors PPI (pressure pulsatility index) and RI (resistance index) calculated in arcuate and radial/spiral arteries are able to differentiate between the 2 groups of women (t-test scores of p < .001) better than PI (pulsatility index) and RI (Doppler calculated in the uterine artery) for both supervised and unsupervised classification. Conclusion – two novel high-performing biomarkers for the classification of pre-eclampsia have been identified based on blood velocity and pressure predictions in the smaller placental vasculatures where non-invasive measurements are not feasible.
published_date 0001-01-01T14:57:35Z
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