Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy

Abstract

<abstract><p>Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves in the diagnosis of hypertensive disorder in pregnancy.This research a retrospective study of pregnant women who attended prenatal care and labored at Beijing Haidian District Maternal and Child Health Hospital. We extracted maternal hemodynamic factors and measured the pulse wave of the pregnant women. We developed an HDP predictive model by using support vector machine algorithms at five-gestational-week stages.At five-gestational-week stages, the area under the receiver operating characteristic curve (AUC) of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors. The AUC values of the predictive model with pulse wave parameters were 0.77 (95% CI 0.64 to 0.9), 0.83 (95% CI 0.77 to 0.9), 0.85 (95% CI 0.81 to 0.9), 0.93 (95% CI 0.9 to 0.96) and 0.88 (95% CI 0.8 to 0.95) at five-gestational-week stages, respectively. Compared to the predictive models with hemodynamic factors, the predictive model with pulse wave parameters had better prediction effects on HDP.Pulse waves had good predictive effects for HDP and provided appropriate guidance and a basis for non-invasive detection of HDP.</p> </abstract>

Transforming Healthcare: The AI Revolution in the Comprehensive Care of Hypertension

“…In the study by Zhang et al [ 31 ] on hypertensive disorders during pregnancy, 168 women were included to compare two predictive models: one using traditional hemodynamic factors like blood pressure and another using pulse wave parameters. The pulse wave model proved more effective, offering better physiological insights into cardiovascular health than blood pressure alone.…”

“…Its accuracy, measured by the Area Under the Curve (AUC), exceeded 80% after the 14th week of pregnancy, peaking between 28 to 34 weeks. Despite its promising results, the study’s limitations include a small sample size and its retrospective nature, indicating the need for larger, prospective studies, but these findings suggest that pulse wave parameters could be a valuable non-invasive tool for early detection and self-monitoring of hypertensive disorders during pregnancy [ 31 ].…”

“…The generalizability and applicability of AI models are hindered by their reliance on heavily unbalanced data, which can lead to biases [ 25 ]. The small sample sizes and retrospective nature of many studies limit the robustness and reliability of the predictive models, necessitating further validation with larger, prospective cohorts [ 31 , 41 ]. Varying laboratory standards and the limited availability of specialized sonographers can also affect the accuracy and consistency of training data and the associated AI models themselves [ 41 ].…”

“… Summary of the applications of AI in the risk prediction, diagnosis, management, and treatment of hypertension [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. …”