Zhou Ling, Tian Yunfan, Su Zhenyang, Sun Jin-Yu, Sun Wei
Department of Obstetrics and Gynecology, Liyang People's Hospital, Liyang, Jiangsu, China.
Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Front Cardiovasc Med. 2024 May 1;11:1272779. doi: 10.3389/fcvm.2024.1272779. eCollection 2024.
Hypertensive disorders of pregnancy (HDP) is a significant cause of maternal and neonatal mortality. This study aims to identify risk factors for new-onset HDP and to develop a prediction model for assessing the risk of new-onset hypertension during pregnancy.
We included 446 pregnant women without baseline hypertension from Liyang People's Hospital at the first inspection, and they were followed up until delivery. We collected maternal clinical parameters and biomarkers between 16th and 20th weeks of gestation. Logistic regression was used to determine the effect of the risk factors on HDP. For model development, a backward selection algorithm was applied to choose pertinent biomarkers, and predictive models were created based on multiple machine learning methods (generalised linear model, multivariate adaptive regression splines, random forest, and k-nearest neighbours). Model performance was evaluated using the area under the curve.
Out of the 446 participants, 153 developed new-onset HDP. The HDP group exhibited significantly higher baseline body mass index (BMI), weight change, baseline systolic/diastolic blood pressure, and platelet counts than the control group. The increase in baseline BMI, weight change, and baseline systolic and diastolic blood pressure significantly elevated the risk of HDP, with odds ratios and 95% confidence intervals of 1.10 (1.03-1.17), 1.10 (1.05-1.16), 1.04 (1.01-1.08), and 1.10 (1.05-1.14) respectively. Restricted cubic spline showed a linear dose-dependent association of baseline BMI and weight change with the risk of HDP. The random forest-based prediction model showed robust performance with the area under the curve of 0.85 in the training set.
This study establishes a prediction model to evaluate the risk of new-onset HDP, which might facilitate the early diagnosis and management of HDP.
妊娠期高血压疾病（HDP）是孕产妇和新生儿死亡的重要原因。本研究旨在确定新发HDP的危险因素，并建立一个预测模型来评估孕期新发高血压的风险。
我们纳入了溧阳市人民医院首次检查时无基线高血压的446名孕妇，并对她们进行随访直至分娩。我们收集了妊娠16至20周期间的孕产妇临床参数和生物标志物。采用逻辑回归确定危险因素对HDP的影响。为了建立模型，应用向后选择算法选择相关生物标志物，并基于多种机器学习方法（广义线性模型、多元自适应回归样条、随机森林和k近邻）创建预测模型。使用曲线下面积评估模型性能。
在446名参与者中，153人发生了新发HDP。HDP组的基线体重指数（BMI）、体重变化、基线收缩压/舒张压和血小板计数显著高于对照组。基线BMI、体重变化以及基线收缩压和舒张压的升高显著增加了HDP的风险，优势比和95%置信区间分别为1.10（1.03 - 1.17）、1.10（1.05 - 1.16）、1.04（1.01 - 1.08）和1.10（1.05 - 1.14）。受限立方样条显示基线BMI和体重变化与HDP风险呈线性剂量依赖关系。基于随机森林的预测模型在训练集中表现出稳健的性能，曲线下面积为0.85。
本研究建立了一个预测模型来评估新发HDP的风险，这可能有助于HDP的早期诊断和管理。
