The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China.
Shantou University Medical College, Shantou, Guangdong, 515041, China.
Hypertens Res. 2023 Sep;46(9):2135-2144. doi: 10.1038/s41440-023-01298-8. Epub 2023 May 9.
In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (LASSO logistic regression, random forest, backpropagation neural network, and support vector machines) to predict the occurrence of PIH in a prospective cohort. Candidate features for predicting the occurrence of middle and late PIH were acquired using a LASSO algorithm. The performance of predictive models was assessed using receiver operating characteristic analysis. Finally, a nomogram was established with the model scores, age, and nulliparity. Calibration, clinical usefulness, and internal validation were used to assess the performance of the nomogram. In the training set (2258 pregnant women), eleven candidate factors in the first trimester were significantly associated with the occurrence of PIH (P < 0.001 in the training set). Four models showed AUCs from 0.780 to 0.816 in the training set. For the validation set (939 pregnant women), AUCs varied from 0.516 to 0.795. The nomogram showed good discrimination, with an AUC of 0.847 (95% CI: 0.805-0.889) in the training set and 0.753 (95% CI: 0.653-0.853) in the validation set. Decision curve analysis suggested that the model was clinically useful. The model developed using LASSO logistic regression achieved the best performance in predicting the occurrence of PIH. The derived nomogram, which incorporates the model score and maternal risk factors, can be used to predict PIH in clinical practice. We develop a model with good performance for clinical prediction of PIH in the first trimester.
在妊娠早期，准确预测妊娠高血压（PIH）的发生对于识别高危妇女和采取早期干预至关重要。本研究采用 LASSO 逻辑回归、随机森林、反向传播神经网络和支持向量机四种机器学习模型对前瞻性队列中 PIH 的发生进行预测。采用 LASSO 算法获取预测中晚期 PIH 发生的候选特征。采用受试者工作特征曲线分析评估预测模型的性能。最后，根据模型评分、年龄和初产妇情况建立列线图。采用校准、临床实用性和内部验证评估列线图的性能。在训练集（2258 名孕妇）中，11 个早期候选因素与 PIH 的发生显著相关（P<0.001）。四个模型在训练集中的 AUC 从 0.780 到 0.816。在验证集（939 名孕妇）中，AUC 从 0.516 到 0.795。列线图显示出良好的判别能力，在训练集和验证集中 AUC 分别为 0.847（95%CI：0.805-0.889）和 0.753（95%CI：0.653-0.853）。决策曲线分析表明该模型具有临床实用性。使用 LASSO 逻辑回归建立的模型在预测 PIH 的发生方面表现最佳。该列线图纳入了模型评分和产妇危险因素，可用于临床实践中预测 PIH。我们建立了一个性能良好的模型，用于预测妊娠早期的 PIH。
