A Machine Learning-Based Prediction Model for Cardiovascular Risk in Women With Preeclampsia.

Guan Wang,Jun Zhang,Yulin Li,Sijin Li,Dongkui Jiang,Jie Du,Yanbo Zhang,Xiuzhen Li

doi:10.3389/fcvm.2021.736491

Abstract

Objective: Preeclampsia affects 2–8% of women and doubles the risk of cardiovascular disease in women after preeclampsia. This study aimed to develop a model based on machine learning to predict postpartum cardiovascular risk in preeclamptic women.Methods: Collecting demographic characteristics and clinical serum markers associated with preeclampsia during pregnancy of 907 preeclamptic women retrospectively, we predicted the cardiovascular risk (ischemic heart disease, ischemic cerebrovascular disease, peripheral vascular disease, chronic kidney disease, metabolic system disease or arterial hypertension). The study samples were divided into training sets and test sets randomly in the ratio of 8:2. The prediction model was developed by 5 different machine learning algorithms, including Random Forest. 10-fold cross-validation was performed on the training set, and the performance of the model was evaluated on the test set.Results: Cardiovascular disease risk occurred in 186 (20.5%) of these women. By weighing area under the curve (AUC), the Random Forest algorithm presented the best performance (AUC = 0.711[95%CI: 0.697–0.726]) and was adopted in the feature selection and the establishment of the prediction model. The most important variables in Random Forest algorithm included the systolic blood pressure, Urea nitrogen, neutrophil count, glucose, and D-Dimer. Random Forest algorithm was well calibrated (Brier score = 0.133) in the test group, and obtained the highest net benefit in the decision curve analysis.Conclusion: Based on the general situation of patients and clinical variables, a new machine learning algorithm was developed and verified for the individualized prediction of cardiovascular risk in post-preeclamptic women.

Highlights

Cardiovascular disease (CVD) is the leading cause of death among women, accounting for one-third of all women’s deaths in the world [1]
The blood pressure, fasting glucose and markers of inflammation, coagulation, renal function after preeclampsia diagnosis were significantly higher than those of patients without CVD (Table 1) Baseline sociodemographic and clinical attributes were generally similar between those who were lost to follow-up and those remained in the sample (Supplementary Table 3)
The most important variable features were selected from a large number of demographic characteristics and simple clinical variables related to preeclampsia diagnosis by Machine learning (ML) methods; a new decision-making tool was trained and tested to predict the occurrence of CVD risk, so as to further investigate the increased CVD risk by preeclampsia

Summary

Introduction

Cardiovascular disease (CVD) is the leading cause of death among women, accounting for one-third of all women’s deaths in the world [1]. The mortality rate of CVD among young women has increased [1]. Preeclampsia is the most serious form of hypertensive disorder of pregnancy. As a gender-specific risk factor for CVD, preeclampsia occurs in 2–8% of pregnant women worldwide and doubles the risk of CVD [2]. Studies have found that preeclampsia and CVD have common risk factors, such as obesity, hypertension, inflammation [3], while preeclampsia may cause long-term changes in blood vessels and metabolism, increasing the risk of postpartum CVD. Current guidelines recommended that clinicians should screen women for pregnancy complications and monitor postpartum risk factors of CVD [6, 7]

Methods

Results

Discussion

Conclusion