天津医药 ›› 2024, Vol. 52 ›› Issue (3): 306-310.doi: 10.11958/20231021

• 临床研究 • 上一篇    下一篇

MP妊高征监测系统联合PLGF和PI对子痫前期的预测价值

张丽冉(), 赵延华()   

  1. 中南大学湘雅医院妇产科(邮编410005)
  • 收稿日期:2023-07-09 修回日期:2023-10-10 出版日期:2024-03-15 发布日期:2024-03-13
  • 通讯作者: E-mail:tz7471@163.com
  • 作者简介:张丽冉(1998),女,硕士在读,主要从事子痫前期预测及发病机制方面研究。E-mail:1352398720@qq.com
  • 基金资助:
    湖南省自然科学基金项目(2020JJ4931)

The predictive value of MP hypertension monitoring system combined with PLGF and PI for preeclampsia

ZHANG Liran(), ZHAO Yanhua()   

  1. Department of Gynaecology and Obstetrics, Xiangya Hospital of Central South University, Changsha 410005, China
  • Received:2023-07-09 Revised:2023-10-10 Published:2024-03-15 Online:2024-03-13
  • Contact: E-mail: tz7471@163.com

摘要:

目的 通过妊娠高血压综合征监测系统测定妊高征风险评级和胎盘生长因子(PLGF)水平,以及胎儿颈项透明层厚度(NT)检查时测得的子宫动脉搏动指数(PI)的联合应用,建立对子痫前期的临床预测模型。方法 选择子痫前期患者24例作为病例组,随机抽取同期有良好妊娠结局的孕妇95例作为对照组,收集2组在孕11~14周免疫荧光定量检测法测定的血清PLGF水平,子宫动脉PI,孕11~20周MP妊高征监测系统风险评级(MP风险)及其他相关数据,记录产前体质量指数(BMI)、年龄、孕次、分娩方式、新生儿出生体质量及Apgar评分。结果 单因素Logistic回归分析结果显示,BMI、年龄、PI、MP风险、PLGF是出现不良结局的影响因素。多因素回归分析结果显示高PI、MP中高风险和PLGF<12是影响出现不良结局的独立危险因素,建立的PE预测模型为logit(P)=-15.767+0.020×PI+0.072×MP风险+0.181×PLGF,ROC曲线下面积(AUC)为0.883,特异度为0.816,敏感度为0.846。结论 联合PI、MP风险、PLGF建立子痫前期临床预测模型具有一定的价值,且其联合预测价值高于单独应用。

关键词: 先兆子痫, 模型, 统计学, 高血压, 妊娠性, 胎盘生长因子, MP妊高征监测系统, 搏动指数

Abstract:

Objective To establish a clinical prediction model for preeclampsia by monitoring risk rating of MP gestation and levels of placental growth factor (PLGF) combined with uterine artery pulsatility index (PI) measured during examination of fetal nuchal translucency (NT). Methods Twenty-four patients with preeclampsia who met the inclusion criteria were selected as the case group, and 95 healthy pregnant women during the same period were randomly selected as the control group. Serum concentrations of PLGF, uterine artery PI values measured by quantitative immunofluorescence assay at 11-14 weeks of gestation, risk ratings for MP hypertension monitoring at 11-20 weeks of gestation, and other relevant data, BMI, age, gestation, mode of delivery, neonatal birth weight and Apgar score were collected in the two groups. Results Results of univariate regression analysis showed that BMI, age, high risk of PI, MP and PLGF<12 were influencing factors for adverse outcomes. Results of multivariate regression analysis showed that high PI, medium high risk in MP and PLGF<12 were independent risk factors for adverse outcomes. The prediction model of PE established was logit (P) = -15.767 + 0.020 × PI + 0.072 × MP risk (medium-high risk = 1, low risk = 0) + 0.181 × PLGF classification (<12 = 1, ≥12 = 0), with an AUC area of 0.883, specificity of 0.816 and sensitivity of 0.846. Conclusion The combination of PI, MP risk and PLGF to establish a clinical predictive model for preeclampsia has certain value, and its combined predictive value is higher than that of single application.

Key words: pre-eclampsia, models, statistical, hypertension, pregnancy-induced, placenta growth factor, MP hypertension monitoring system, pulsatility index

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