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

• Published in: BMC cardiovascular disorders Vol. 20; no. 1; p. 155
• Main Authors: Hou, Yamin, Yun, Lin, Zhang, Lihua, Lin, Jingru, Xu, Rui
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.04.2020; BioMed Central; BMC
• Subjects: Age; Blood pressure; Body mass index; Cardiovascular diseases; Cystatin C; Demographic aspects; Diagnosis; Disease; Family medical history; Gestational age; Gestational hypertension; Glomerular filtration rate; Han (Chinese people); Health aspects; Hemoglobin; Homocysteine; Hypertension; Hypertension, pregnancy induced; Neonates; Obstetrics; Pre-eclampsia; Prediction model; Prediction models; Preeclampsia; Pregnancy; Risk factors; Statistical analysis; Uric acid; Variables; Womens health; China; Hypertension, pregnancy induced; Homocysteine; Risk factors; Prediction model
• ISSN: 1471-2261; 1471-2261
• DOI: 10.1186/s12872-020-01428-x

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. 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. 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. 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.