Jiang Ying, Chen Lu-Jiao, Hu Hui-Hui, Jin Neng, Lv Shi-Rui, Fang Chen, Zhu Chun-Mei, Yang Meng-Meng, Xu Dong, Luo Qiong
Department of Obstetrics, School of Medicine, Women's Hospital, Zhejiang University, Shangcheng District, No.1, Xueshi Road, Hangzhou, 310006, China.
Zhejiang Provincial Clinical Research Center for Child Health, Hangzhou, 310006, China.
BMC Pregnancy Childbirth. 2025 Apr 10;25(1):418. doi: 10.1186/s12884-025-07546-8.
The unpredictability of HELLP syndrome and the severe adverse outcomes for both mother and children make it especially important for us to seek predictive model. This study aimed to develop a clinically accessible prediction model for assessing the risk of HELLP syndrome progression in patients with hypertensive disorders of pregnancy (HDP) and find effective factors that may predict the progression of HELLP within 3 days.
We used electronic data from Women's Hospital, Zhejiang University School of Medicine, between January 1,2014 and December 31,2023. A total of 808 patients were included in this study, including 607 patients in the non-HELLP syndrome group and 201 patients in the HELLP syndrome group. We collected clinical and laboratory information, and conducted single- and multiple-factor logistic regression analyses to identify independent factors influencing the occurrence of HELLP syndrome and the onset of HELLP syndrome within 3 days. A nomogram was constructed based on these predictors to provide a visual representation of risk estimation. The model's performance was evaluated through internal and external validation, with metrics such as the area under the curve(AUC), receiver operating characteristic curve (ROC), precision, recall, and F1 score. Calibration and decision curve analyses were also performed to assess model robustness and clinical utility.
Multiple logistic regression analysis indicated prenatal BMI, neurologic symptoms, other system symptoms, 24-h urine protein, lowest SBP at admission, lowest DBP at admission, prenatal albumin, prenatal platelet and prenatal blood urea nitrogen as independent factors of HELLP syndrome. The prediction model achieved an AUC of 0.975 (95% CI: 0.966-0.985) in the internal validation dataset with a sensitivity of 0.962(95% CI: 0.962-1.000) and specificity of 0.885(95% CI: 0.962-1.000). The AUC of the external validation dataset was 0.838 (95% CI: 0.785-0.892). The optimal cutoff value calculated using Youden's index was 0.613, with a sensitivity of 0.891(95% CI: 0.473-0.836) and specificity of 0.722(95% CI: 0.667-0.818). In multivariate regression analysis, blood urea nitrogen and the creatinine-to-blood urea nitrogen ratio were significant predictors in predicting HELLP syndrome within 3 days. The sensitivity was found to be 0.68 and 0.65, specificity to be 0.74 and 0.686 respectively.
This study successfully developed and validated a prediction model that can reliably predict the risk of HELLP syndrome in HDP patients. And blood urine nitrogen and the ratio of creatinine over blood urea nitrogen could be efficient predictors of HELLP syndrome occurring within 3 days.
HELLP综合征的不可预测性以及对母婴的严重不良后果，使得寻找预测模型对我们尤为重要。本研究旨在建立一种临床可用的预测模型，用于评估妊娠高血压疾病（HDP）患者发生HELLP综合征进展的风险，并找出可能预测3天内HELLP进展的有效因素。
我们使用了浙江大学医学院附属妇产科医院2014年1月1日至2023年12月31日的电子数据。本研究共纳入808例患者，其中非HELLP综合征组607例，HELLP综合征组201例。我们收集了临床和实验室信息，并进行单因素和多因素逻辑回归分析，以确定影响HELLP综合征发生及3天内HELLP综合征发病的独立因素。基于这些预测因素构建了列线图，以提供风险估计的直观表示。通过内部和外部验证评估模型的性能，指标包括曲线下面积（AUC）、受试者工作特征曲线（ROC）、精度、召回率和F1分数。还进行了校准和决策曲线分析，以评估模型的稳健性和临床实用性。
多因素逻辑回归分析表明，产前体重指数、神经系统症状、其他系统症状、24小时尿蛋白、入院时最低收缩压、入院时最低舒张压、产前白蛋白、产前血小板和产前血尿素氮是HELLP综合征的独立因素。预测模型在内部验证数据集中的AUC为0.975（95%CI：0.966-0.985），敏感性为0.962（95%CI：0.962-1.000），特异性为0.885（95%CI：0.962-1.000）。外部验证数据集的AUC为0.838（95%CI：0.785-0.892）。使用约登指数计算的最佳截断值为0.613，敏感性为0.891（95%CI：0.473-0.836），特异性为0.722（95%CI：0.667-0.818）。在多变量回归分析中，血尿素氮和肌酐与血尿素氮比值是预测3天内HELLP综合征的重要预测因素。敏感性分别为0.68和0.65，特异性分别为0.74和0.686。
本研究成功开发并验证了一种预测模型，该模型能够可靠地预测HDP患者发生HELLP综合征的风险。血尿素氮和肌酐与血尿素氮比值可能是3天内发生HELLP综合征的有效预测指标。
