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. 2020 Apr 3;20(1):155.
doi: 10.1186/s12872-020-01428-x.

A risk factor-based predictive model for new-onset hypertension during pregnancy in Chinese Han women

Yamin Hou  1   2 , Lin Yun  3 , Lihua Zhang  3 , Jingru Lin  4 , Rui Xu  5   6
Affiliations

A risk factor-based predictive model for new-onset hypertension during pregnancy in Chinese Han women

Yamin Hou et al. BMC Cardiovasc Disord. .

Abstract

Background: Hypertensive disorders of pregnancy (HDP) is one of the leading causes of maternal and neonatal mortality, increasing the long-term incidence of cardiovascular diseases. Preeclampsia and gestational hypertension are the major components of HDP. The aim of our study is to establish a prediction model for pregnant women with new-onset hypertension during pregnancy (increased blood pressure after gestational age > 20 weeks), thus to guide the clinical prediction and treatment of de novo hypertension.

Methods: A total of 117 pregnant women with de novo hypertension who were admitted to our hospital's obstetrics department were selected as the case group and 199 healthy pregnant women were selected as the control group from January 2017 to June 2018. Maternal clinical parameters such as age, family history and the biomarkers such as homocysteine, cystatin C, uric acid, total bile acid and glomerular filtration rate were collected at a mean gestational age in 16 to 20 weeks. The prediction model was established by logistic regression.

Results: Eleven indicators have statistically significant difference between two groups (P < 0.05). These 11 factors were substituted into the logistic regression equation and 7 independent predictors were obtained. The equation expressed including 7 factors. The calculated area under the curve was 0.884(95% confidence interval: 0.848-0.921), the sensitivity and specificity were 88.0 and 75.0%. A scoring system was established to classify pregnant women with scores ≤15.5 as low-risk pregnancy group and those with scores > 15.5 as high-risk pregnancy group.

Conclusions: Our regression equation provides a feasible and reliable means of predicting de novo hypertension after pregnancy. Risk stratification of new-onset hypertension was performed to early treatment interventions in high-risk populations.

Keywords: Homocysteine; Hypertension, pregnancy induced; Prediction model; Risk factors.

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Conflict of interest statement

No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

Figures

Fig. 1
Fig. 1
ROC curve of binary logistic regression. The AUC was 0.884(95%CI: 0.848–0.921). The sensitivity is 88.0%, and the specificity is 75.0%
Fig. 2
Fig. 2
ROC curve of scoring system. The AUC was 0.880(95%CI 0.842–0.918). The sensitivity is 89.7%, the specificity is 70.9%
See this image and copyright information in PMC

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References

    1. Kobashi G. Genetic and environmental factors associated with the development of hypertension in pregnancy. J Epidemiol. 2006;16(1):1–8. doi: 10.2188/jea.16.1. - DOI - PMC - PubMed
    1. Ye C, Ruan Y, Zou L, Li G, Li C, Chen Y, et al. The 2011 survey on hypertensive disorders of pregnancy (HDP) in China: prevalence, risk factors, complications, pregnancy and perinatal outcomes. PLoS One. 2014;9(6):e100180. doi: 10.1371/journal.pone.0100180. - DOI - PMC - PubMed
    1. Brown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, et al. Hypertensive disorders of pregnancy: ISSHP classification, diagnosis, and management recommendations for international practice. Hypertension. 2018;72(1):24–43. doi: 10.1161/HYPERTENSIONAHA.117.10803. - DOI - PubMed
    1. Umesawa M, Kobashi G. Epidemiology of hypertensive disorders in pregnancy: prevalence, risk factors, predictors and prognosis. Hypertens Res. 2017;40(3):213–220. doi: 10.1038/hr.2016.126. - DOI - PubMed
    1. Morikawa M, Yamada T, Yamada T, Cho K, Sato S, Minakami H. Seasonal variation in the prevalence of pregnancy-induced hypertension in Japanese women. J Obstet Gynaecol Res. 2014;40(4):926–931. doi: 10.1111/jog.12304. - DOI - PubMed

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