Biomedical Engineering Simulation and Testing Lab, Department of Biomedical Engineering, Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN, UK.
Division of Developmental Biology, Maternal and Fetal Health Research Centre, Faculty of Medicine Biology and Health, University of Manchester, Manchester, UK.
Sci Rep. 2024 Oct 4;14(1):23144. doi: 10.1038/s41598-024-72832-y.
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.
计算模型可以作为研究和分类疾病（如子痫前期）的新技术的基础，在这种疾病中，当患者有高血压病史时，很难根据压力区分子痫前期患者和非子痫前期患者。计算模型现在可以对妊娠如何影响心血管系统进行详细分析。因此，开发了新的非侵入性生物标志物，可以通过整合六种不同的测量非侵入性心血管信号来辅助子痫前期的分类。使用 21 名孕妇（无早发性子痫前期，n = 12；早发性子痫前期，n = 9）的数据集，通过计算建模创建个性化心血管模型，从而预测子宫-卵巢系统中所有主要和次要血管的血压和流量波形。进行的分析表明，在弓形动脉和桡动脉/螺旋动脉中计算的新预测因子 PPI（压力脉动指数）和 RI（阻力指数）比 PI（在子宫动脉中计算的搏动指数）和 RI（Doppler 计算的在子宫动脉中）更能区分两组女性（t 检验分数 p <.001），无论是在监督分类还是无监督分类中。总之，已经基于在较小的胎盘脉管系统中进行的血流速度和压力预测，确定了两种用于子痫前期分类的新型高性能生物标志物，这些生物标志物在非侵入性测量不可行的情况下是可行的。
