Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models
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. Computatio...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-72832-y |
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| author | Claudia Popp Jason M. Carson Alex B. Drysdale Hari Arora Edward D. Johnstone Jenny E. Myers Raoul van Loon |
| author_facet | Claudia Popp Jason M. Carson Alex B. Drysdale Hari Arora Edward D. Johnstone Jenny E. Myers Raoul van Loon |
| author_sort | Claudia Popp |
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| description | 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. Therefore, new non-invasive biomarkers were developed that can aid the classification of pre-eclampsia through the integration of six different measured non-invasive cardiovascular signals. 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. 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. In 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. |
| format | Article |
| id | doaj-art-7e0a96b845e446fc85ae85cf0c5a0b1d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-7e0a96b845e446fc85ae85cf0c5a0b1d2025-08-20T03:45:52ZengNature PortfolioScientific Reports2045-23222024-10-011411810.1038/s41598-024-72832-yDevelopment of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network modelsClaudia Popp0Jason M. Carson1Alex B. Drysdale2Hari Arora3Edward D. Johnstone4Jenny E. Myers5Raoul van Loon6Biomedical Engineering Simulation and Testing Lab, Department of Biomedical Engineering, Faculty of Science and Engineering, Swansea UniversityBiomedical Engineering Simulation and Testing Lab, Department of Biomedical Engineering, Faculty of Science and Engineering, Swansea UniversityBiomedical Engineering Simulation and Testing Lab, Department of Biomedical Engineering, Faculty of Science and Engineering, Swansea UniversityBiomedical Engineering Simulation and Testing Lab, Department of Biomedical Engineering, Faculty of Science and Engineering, Swansea University Division of Developmental Biology, Maternal and Fetal Health Research Centre, Faculty of Medicine Biology and Health, University of Manchester Division of Developmental Biology, Maternal and Fetal Health Research Centre, Faculty of Medicine Biology and Health, University of ManchesterBiomedical Engineering Simulation and Testing Lab, Department of Biomedical Engineering, Faculty of Science and Engineering, Swansea UniversityAbstract 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. Therefore, new non-invasive biomarkers were developed that can aid the classification of pre-eclampsia through the integration of six different measured non-invasive cardiovascular signals. 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. 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. In 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.https://doi.org/10.1038/s41598-024-72832-yUterine doppler waveformsPregnancyPulse wave velocityHypertensionMachine learningClinical diagnosis |
| spellingShingle | Claudia Popp Jason M. Carson Alex B. Drysdale Hari Arora Edward D. Johnstone Jenny E. Myers Raoul van Loon Development of non-invasive biomarkers for pre-eclampsia through data-driven cardiovascular network models Scientific Reports Uterine doppler waveforms Pregnancy Pulse wave velocity Hypertension Machine learning Clinical diagnosis |
| title | 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_short | 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 |
| topic | Uterine doppler waveforms Pregnancy Pulse wave velocity Hypertension Machine learning Clinical diagnosis |
| url | https://doi.org/10.1038/s41598-024-72832-y |
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