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Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
,
Alexander Drysdale,
Hari Arora ,
Edward D. Johnstone,
Jenny E. Myers,
Raoul van Loon
Scientific Reports, Volume: 14, Issue: 1
Swansea University Authors:
Claudia Popp, Jason Carson , Alexander Drysdale, Hari Arora , Raoul van Loon
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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...
| Published in: | Scientific Reports |
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| ISSN: | 2045-2322 |
| Published: |
Springer Science and Business Media LLC
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa67777 |
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| 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 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. |
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| Keywords: |
Uterine doppler waveforms, Pregnancy, Pulse wave velocity, Hypertension, Machine learning, Clinical diagnosis, Digital twin |
| College: |
Faculty of Science and Engineering |
| Funders: |
This work is supported by Wellcome Leap as part of the In Utero Program. AD is funded by a UKAEA research PhD scholarship. |
| Issue: |
1 |