Department of Obstetrics and Gynecology, Jinshan Branch of Shanghai Sixth People's Hospital, Shanghai, China.
Department of Laboratory, Jinshan Branch of Shanghai Sixth People's Hospital, Shanghai, China.
Front Endocrinol (Lausanne). 2024 Jan 8;14:1297731. doi: 10.3389/fendo.2023.1297731. eCollection 2023.
This study analyzes the levels of peripheral blood placental growth factor (PLGF), body mass index (BMI), decorin (DCN), lactate dehydrogenase (LDH), uric acid (UA), and clinical indicators of patients with preeclampsia (PE), and establishes a predictive risk model of PE, which can provide a reference for early and effective prediction of PE.
81 cases of pregnant women with PE who had regular prenatal checkups and delivered in Jinshan Branch of Shanghai Sixth People's Hospital from June 2020 to December 2022 were analyzed, and 92 pregnant women with normal pregnancies who had their antenatal checkups and delivered at the hospital during the same period were selected as the control group. Clinical data and peripheral blood levels of PLGF, DCN, LDH, and UA were recorded, and the two groups were subjected to univariate screening and multifactorial logistic regression analysis. Based on the screening results, the diagnostic efficacy of PE was evaluated using the receiver operating characteristic (ROC) curve. Risk prediction nomogram model was constructed using language. The Bootstrap method (self-sampling method) was used to validate and produce calibration plots; the decision curve analysis (DCA) was used to assess the clinical benefit rate of the model.
There were statistically significant differences in age, pre-pregnancy BMI, gestational weight gain, history of PE or family history, family history of hypertension, gestational diabetes mellitus, and history of renal disease between the two groups (P < 0.05). The results of multifactorial binary logistic stepwise regression revealed that peripheral blood levels of PLGF, DCN, LDH, UA, and pre-pregnancy BMI were independent influences on the occurrence of PE (P < 0.05). The area under the curve of PLGF, DCN, LDH, UA levels and pre-pregnancy BMI in the detection of PE was 0.952, with a sensitivity of 0.901 and a specificity of 0.913, which is better than a single clinical diagnostic indicator. The results of multifactor analysis were constructed as a nomogram model, and the mean absolute error of the calibration curve of the modeling set was 0.023, suggesting that the predictive probability of the model was generally compatible with the actual value. DCA showed the predictive model had a high net benefit in the range of 5% to 85%, suggesting that the model has clinical utility value.
The occurrence of PE is related to the peripheral blood levels of PLGF, DCN, LDH, UA and pre-pregnancy BMI, and the combination of these indexes has a better clinical diagnostic value than a single index. The nomogram model constructed by using the above indicators can be used for the prediction of PE and has high predictive efficacy.
本研究分析了子痫前期（PE）患者外周血胎盘生长因子（PLGF）、体重指数（BMI）、核心蛋白聚糖（DCN）、乳酸脱氢酶（LDH）、尿酸（UA）等临床指标水平，并建立了PE 的预测风险模型，为 PE 的早期、有效预测提供参考。
分析 2020 年 6 月至 2022 年 12 月在上海市第六人民医院金山分院定期产检并分娩的 81 例 PE 孕妇，选择同期在该院产检并分娩的 92 例正常妊娠孕妇作为对照组。记录临床资料及外周血 PLGF、DCN、LDH、UA 水平，对两组进行单因素筛选和多因素 logistic 回归分析，基于筛选结果采用受试者工作特征（ROC）曲线评估 PE 诊断效能。使用 R 语言构建风险预测列线图模型，采用自助抽样法（Bootstrap 方法）对模型进行验证并生成校准图；采用决策曲线分析（DCA）评估模型的临床获益率。
两组孕妇年龄、孕前 BMI、孕期体重增长、PE 史或家族史、高血压家族史、妊娠期糖尿病史、肾病史比较，差异有统计学意义（P<0.05）。多因素二项逐步 logistic 回归结果显示，外周血 PLGF、DCN、LDH、UA 水平及孕前 BMI 是影响 PE 发生的独立因素（P<0.05）。PLGF、DCN、LDH、UA 水平及孕前 BMI 联合检测对 PE 的诊断曲线下面积为 0.952，敏感度为 0.901，特异度为 0.913，均优于单一临床诊断指标。多因素分析结果构建为列线图模型，建模集校准曲线的平均绝对误差为 0.023，提示模型预测概率与实际值大体吻合。DCA 显示预测模型在 5%~85%的范围内具有较高的净获益，提示模型具有临床应用价值。
PE 的发生与外周血 PLGF、DCN、LDH、UA 及孕前 BMI 水平有关，联合这些指标的临床诊断价值优于单一指标。应用上述指标构建的列线图模型可用于预测 PE，具有较高的预测效能。
