Predicting hypertensive disorders in pregnancy using multiple methods: Models with the placental growth factor parameter

1.Introduction

Hypertensive disorders in pregnancy (HDP) is an important risk factor for increasing neonatal and maternal morbidity and mortality [1, 2, 3]. Early prediction and treatment can be carried out through related risk factors [4]. Preeclampsia in HDP is one of the most serious pregnancy complications [5, 6]. Studies have shown that placental growth factor (PlGF) was related to the diagnosis of HDP [7]. Nguyen et al. studied the predictive value of Soluble fms-Like Tyrosine Kinase 1 (sFlt-1) and PlGF for women at high risk of preeclampsia [8]. Combining maternal risk factors, mean arterial pressure (MAP), PlGF, and uterine artery pulsatility index (UTPI) for related prediction accuracy was higher [9]. Bian et al. used sFlt-1/PlGF ratio to predict the risk of preeclampsia in Asian women [10]. PlGF combined with other angiogenesis markers, such as sFlt-1, also had a certain prognostic value for preeclampsia [11, 12]. There are also some controversies in related prediction research. Cnossen et al. found that the predictive value of uterine artery Doppler studies alone for early and late onset preeclampsia was very low [13]. No test could reliably predict preeclampsia, and further prospective studies were needed to prove the clinical utility of predictors [14].

A large number of foreign studies have confirmed the role of PlGF in predicting HDP. Such as using maternal factors plus biomarkers (PlGF, etc.) for prediction. But the associated clinical utility was unclear. For PlGF, one of biomarkers, the appropriate range of PlGF parameters included in the predictive model needed to be further selected. Moreover, variable screening methods were mostly based on the statistical indicator (P value), rather than comprehensive screening of risk factors. The data analysis method of this study mainly included two aspects: variable selection and model methods. Model parameters were screened based on the effects of various models containing PlGF, and several model methods were comprehensively selected to compare the prediction effects.

2.Materials and methods

3.Results

In this study, the categories of predictive model parameters were derived from Table 1. For the model containing PlGF, the situation before the screening in 2.3.1 and after the screening in 2.3.2 was compared, as shown in Tables 3 and 4. Except for PlGF, all the other parameters (see Table 1) existed consistently before being included in the prediction model for automatic variable screening in 2.3.1 and 2.3.2.

For the models without PlGF screening in Table 3, the step-logistic regression test of PlGF2 mentioned in 2.3.1 showed no statistical difference. In Table 4, both methods put the relative outliers of PlGF at 15–26 weeks into account. Each model index has been improved, and it was statistically significant to test PlGF in the step-logistic regression (P< 0.05). Especially the sensitivity, both methods have reached more than 92%. The final model selected the lasso method in Table 4, as shown in Table 5.

4.Discussion

Some angiogenic factors (Soluble fms-Like Tyrosine Kinase 1 (sFlt-1), placental growth factor (PlGF), and Soluble endothelin) in the second trimester may be tools for predicting preeclampsia [14]. Numerous studies have demonstrated that sFlt-1 and PlGF can play a role in the prediction of early preeclampsia in the second trimester [15]. Knudsen et al. also affirmed the independent predictive effect of PlGF [16]. The levels of sFlt-1 and PlGF in pregnant women in Malaysia could be used as biochemical indicators of gestational hypertension [17]. As a predictive marker of preeclampsia, PlGF could simplify the clinical management of preeclampsia and reduced costs [18].

This study used maternal basic factors and PlGF, and also confirmed the predictive role of PlGF in the second trimester. Based on the quality and effect of the model, comprehensive variable screening and modeling analysis and prediction were carried out.

5.Conclusion

In addition to basic statistical analysis, this research had comprehensive advantages in variable selection and model building. Maternal factors and biomarker PlGF were combined to predict. Based on the model and clinical needs, a comprehensive screening analysis was carried out to select the optimal prediction model plan containing the PlGF parameter. Finally, the PlGF value of 15–26 weeks (the second trimester) was selected for model research containing the PlGF parameter. The PlGF test in step-logistic regression was statistically significant (P< 0.05). Moreover, the comprehensive indicators such as area under the curve (AUC), sensitivity, and specificity of the model have been improved. In particular, the sensitivity of the two methods reached about 93%. Finally, the model parameters of lasso method were comprehensively selected for final HDP prediction. Future studies will need to increase the number of PlGF tests at full gestational age and increase the variety of risk factors.