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中华产科急救电子杂志 ›› 2021, Vol. 10 ›› Issue (04) : 201 -205. doi: 10.3877/cma.j.issn.2095-3259.2021.04.003

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再谈子痫前期的预测
储倩倩1, 张羽1,()   
  1. 1. 200127 上海交通大学医学院附属仁济医院妇产科
  • 收稿日期:2021-08-20 出版日期:2021-11-18
  • 通信作者: 张羽

The prediction of preeclampsia

Qianqian Chu1, Yu Zhang1()   

  • Received:2021-08-20 Published:2021-11-18
  • Corresponding author: Yu Zhang
引用本文:

储倩倩, 张羽. 再谈子痫前期的预测[J/OL]. 中华产科急救电子杂志, 2021, 10(04): 201-205.

Qianqian Chu, Yu Zhang. The prediction of preeclampsia[J/OL]. Chinese Journal of Obstetric Emergency(Electronic Edition), 2021, 10(04): 201-205.

子痫前期的预测一直是产科学界关注的重点。目前国内外指南中根据孕妇既往病史及临床特征进行子痫前期风险评估和预测,并据此作为阿司匹林等预防性用药的依据。近年来人们对现有的预测指标和模型开展了大量临床研究,发现以孕妇特征、平均动脉压、子宫动脉搏动指数及血清胎盘生长因子等多指标联合子痫前期预测模型,预测的准确性提高。FullPIERS和PREP-S模型可用于预测子痫前期患者的不良妊娠结局,对于患者终止妊娠及转诊时机管理有重要意义。

The prediction of preeclampsia (PE) has always caught the eyes of obstetricians. In the present domestic and foreign guidelines, according to the previous medical history and clinical characteristics of pregnant women, the risk assessment and prediction of preeclampsia are carried out, and which are used as the basis of aspirin and other preventive drugs. Recently, a large number of clinical studies have been conducted on existing predictive indicators and models. It shows that the combined predictive model of preeclampsia based on maternal characteristics, mean arterial pressure, uterine artery pulse index and serum placental growth factor could improve the accuracy of prediction. FullPIERS and PREP-S models can be used to screen adverse pregnancy outcomes in PE patients, which is of great significance for the management of pregnancy termination and referral time.

[1]
American College of Obstetricians and Gynecologists. ACOG Practice Bulletin No. 202: Gestational Hypertension and Preeclampsia [J]. Obstet Gynecol2019133(1):e1-e25.
[2]
Webster K, Fishburn S, Maresh M, et al. Diagnosis and management of hypertension in pregnancy: summary of updated NICE guidance [J]. BMJ, 2019, 366:1-8.
[3]
中华医学会妇产科学会分会妊娠期高血压疾病学组.妊娠期高血压疾病诊治指南(2020)[J]. 中华妇产科杂志2020, 55(4): 227-238.
[4]
Boutin A, Gasse C, Demers S, et al. Maternal Characteristics for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndromes (GOS) Study [J].J Obstet Gynaecol Can, 2018, 40(5):572-578.
[5]
O′Gorman N, Wright D, Poon LC, et al. Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks′ gestation: comparison with NICE guidelines and ACOG recommendations [J]. Ultrasound Obstet Gynecol, 2017, 49(6):756-760.
[6]
苏镇培.谈"平均动脉压"——兼答读者问[J].中华高血压杂志2015, 23(8):719-721.
[7]
Poon LC, Kametas NA, Valencia C, et al. Hypertensive Disorders in Pregnancy: Screening by Systolic Diastolic and Mean Arterial Pressure at 11-13 Weeks [J]. Hypertension in Pregnancy, 2011, 30(1):93-107.
[8]
Gasse C, Boutin A, Cote M, et al. First-trimester mean arterial blood pressure and the risk of preeclampsia: The Great Obstetrical Syndromes (GOS) study [J]. Pregnancy Hypertens201812:178-182.
[9]
Rocha RS, Alves JAG, Maia E, Holanda Moura SB, et al. Simple approach based on maternal characteristics and mean arterial pressure for the prediction of preeclampsia in the first trimester of pregnancy[J]. J Perinat Med, 201745(7):843-849.
[10]
Agrawal S, Shinar S, Cerdeira AS, et al. Predictive Performance of PlGF (Placental Growth Factor) for Screening Preeclampsia in Asymptomatic Women: A Systematic Review and Meta-Analysis [J]. Hypertension, 201974(5):1124-1135.
[11]
Boutin A, Demers S, Gasse C, et al. First-Trimester Placental Growth Factor for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndromes Cohort Study [J]. Fetal Diagn Ther, 2019, 45(2):69-75.
[12]
Agrawal S, Cerdeira AS, Redman C, et al. Meta-Analysis and Systematic Review to Assess the Role of Soluble FMS-Like Tyrosine Kinase-1 and Placenta Growth Factor Ratio in Prediction of Preeclampsia: The SaPPPhirE Study [J]. Hypertension, 2018, 71(2): 306-316.
[13]
Bian X, Biswas A, Huang X, et al. Short-Term Prediction of Adverse Outcomes Using the sFlt-1 (Soluble fms-Like Tyrosine Kinase 1)/PlGF (Placental Growth Factor) Ratio in Asian Women With Suspected Preeclampsia [J]. Hypertension, 2019, 74(1):164-172.
[14]
Boutin A, Gasse C, Demers S, et al. Does Low PAPP-A Predict Adverse Placenta-Mediated Outcomes in a Low-Risk Nulliparous Population? the Great Obstetrical Syndromes (GOS) Study [J]. J Obstet Gynaecol Can, 2018, 40(6):663-668.
[15]
De Villiers CP, Hedley PL, Placing S, et al. Placental protein-13 (PP13) in combination with PAPP-A and free leptin index (fLI) in first trimester maternal serum screening for severe and early preeclampsia [J]. Clin Chem Lab Med, 2017, 56(1):65-74.
[16]
刘楼,许建娟,孙丽洲. 彩色多普勒超声测定胎盘商与胎盘蛋白-13联合筛查预测子痫前期的价值 [J].中华超声影像学杂志2016, 25(5): 453-454.
[17]
Demers S, Boutin A, Gasse C, et al. First-Trimester Uterine Artery Doppler for the Prediction of Preeclampsia in Nulliparous Women: The Great Obstetrical Syndrome Study [J]. Am J Perinatol, 2019, 36(9):930-935.
[18]
González-González NL, González Dávila E, Padrón E, et al. Value of Placental Volume and Vascular Flow Indices as Predictors of Early and Late Preeclampsia at First Trimester [J]. Fetal Diagn Ther, 2018, 44(4):256-263.
[19]
Akolekar R, Syngelaki A, Poon L, et al. Competing risks model in early screening for preeclampsia by biophysical and biochemical markers [J]. Fetal Diagn Ther, 2013, 33(1): 8-15.
[20]
Tan MY, Wright D, Syngelaki A, et al. Comparison of diagnostic accuracy of early screening for pre-eclampsia by NICE guidelines and a method combining maternal factors and biomarkers: results of SPREE[J]. Ultrasound Obstet Gynecol, 2018, 51(6): 743-750.
[21]
Risk for preeclampsia[DB/OL].London:Fetal Medicine Fundation,2021[2021-08-20].

URL    
[22]
Chaemsaithong P, Pooh RK, Zheng M, et al. Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population[J]. Am J Obstet Gynecol, 2019, 221(6): 650.e1-650.e16.
[23]
O′gorman N, Wright D, Poon LC, et al. Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks′ gestation: comparison with NICE guidelines and ACOG recommendations[J]. Ultrasound Obstet Gynecol, 2017, 49(6): 756-760.
[24]
Poon LC, Shennan A, Hyett JA, et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention[J]. Int J Gynaecol Obstet, 2019, 145 Suppl 1(Suppl 1): 1-33.
[25]
Von Dadelszen P, Payne B, Li J, et al. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model[J]. Lancet, 2011, 377(9761): 219-227.
[26]
Thangaratinam S, Allotey J, Marlin N, et al. Prediction of complications in early-onset pre-eclampsia (PREP): development and external multinational validation of prognostic models[J]. BMC Med, 2017, 15(1): 68.
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