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

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

Scientific Reports, Volume: 14, Issue: 1

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

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DOI (Published version): 10.1038/s41598-024-72832-y

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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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67777
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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 &lt; .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.</abstract><type>Journal Article</type><journal>Scientific Reports</journal><volume>14</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2045-2322</issnElectronic><keywords>Uterine doppler waveforms, Pregnancy, Pulse wave velocity, Hypertension, Machine learning, Clinical diagnosis, Digital twin</keywords><publishedDay>4</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-10-04</publishedDate><doi>10.1038/s41598-024-72832-y</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><apcterm>Other</apcterm><funders>This work is supported by Wellcome Leap as part of the In Utero Program. AD is funded by a UKAEA research PhD scholarship.</funders><projectreference/><lastEdited>2024-10-21T13:19:45.8578081</lastEdited><Created>2024-09-24T15:13:51.3518863</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Biomedical Engineering</level></path><authors><author><firstname>Claudia</firstname><surname>Popp</surname><order>1</order></author><author><firstname>Jason</firstname><surname>Carson</surname><orcid>0000-0001-6634-9123</orcid><order>2</order></author><author><firstname>Alexander</firstname><surname>Drysdale</surname><order>3</order></author><author><firstname>Hari</firstname><surname>Arora</surname><orcid>0000-0002-9790-0907</orcid><order>4</order></author><author><firstname>Edward D.</firstname><surname>Johnstone</surname><order>5</order></author><author><firstname>Jenny E.</firstname><surname>Myers</surname><order>6</order></author><author><firstname>Raoul</firstname><surname>van Loon</surname><orcid>0000-0003-3581-5827</orcid><order>7</order></author></authors><documents><document><filename>67777__32658__0d4303db740944918882304c53d157da.pdf</filename><originalFilename>67777.VoR.pdf</originalFilename><uploaded>2024-10-21T13:14:46.4300851</uploaded><type>Output</type><contentLength>1985632</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© The Author(s) 2024. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by-nc-nd/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling v2 67777 2024-09-24 Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models 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 880b30f90841a022f1e5bac32fb12193 0000-0003-3581-5827 Raoul van Loon Raoul van Loon true false 2024-09-24 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 14 1 Springer Science and Business Media LLC 2045-2322 Uterine doppler waveforms, Pregnancy, Pulse wave velocity, Hypertension, Machine learning, Clinical diagnosis, Digital twin 4 10 2024 2024-10-04 10.1038/s41598-024-72832-y COLLEGE NANME COLLEGE CODE Swansea University Other This work is supported by Wellcome Leap as part of the In Utero Program. AD is funded by a UKAEA research PhD scholarship. 2024-10-21T13:19:45.8578081 2024-09-24T15:13:51.3518863 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Claudia Popp 1 Jason Carson 0000-0001-6634-9123 2 Alexander Drysdale 3 Hari Arora 0000-0002-9790-0907 4 Edward D. Johnstone 5 Jenny E. Myers 6 Raoul van Loon 0000-0003-3581-5827 7 67777__32658__0d4303db740944918882304c53d157da.pdf 67777.VoR.pdf 2024-10-21T13:14:46.4300851 Output 1985632 application/pdf Version of Record true © The Author(s) 2024. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
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
Claudia Popp
Jason Carson
Alexander Drysdale
Hari Arora
Raoul van Loon
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 1e879f6786330055618b55abbef1569c
c1f2c28fbe6a41c5134b45abde5abb93
59f357e91ed91f03597ac28978e6bc30
ed7371c768e9746008a6807f9f7a1555
880b30f90841a022f1e5bac32fb12193
author_id_fullname_str_mv 1e879f6786330055618b55abbef1569c_***_Claudia Popp
c1f2c28fbe6a41c5134b45abde5abb93_***_Jason Carson
59f357e91ed91f03597ac28978e6bc30_***_Alexander Drysdale
ed7371c768e9746008a6807f9f7a1555_***_Hari Arora
880b30f90841a022f1e5bac32fb12193_***_Raoul van Loon
author Claudia Popp
Jason Carson
Alexander Drysdale
Hari Arora
Raoul van Loon
author2 Claudia Popp
Jason Carson
Alexander Drysdale
Hari Arora
Edward D. Johnstone
Jenny E. Myers
Raoul van Loon
format Journal article
container_title Scientific Reports
container_volume 14
container_issue 1
publishDate 2024
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-024-72832-y
publisher Springer Science and Business Media LLC
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 1
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 2024-10-04T13:19:44Z
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