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目的探讨中孕早期联合母体特征、平均动脉压(MAP)及血清学标记物预测子痫前期(PE)的价值。方法前瞻性留取2014年5月至2015年5月孕妇行中期唐氏筛查时的血样,追踪妊娠结局,获取重症子痫前期(SPE)31例,非重症子痫前期(Non-SPE)29例,妊高(GH)35例和正常妊娠(NP)120例,比较疾病组和对照组的母体特征,MAP及7项血清学指标。应用Logistic和ROC曲线分析各指标单独或联合时预测PE的敏感度和特异度,构建预测PE的最优模型。结果 (1)与NP组比较,SPE组的母龄、Non-SPE和GH组的BMI明显增大(均P<0.01);(2)PE和GH组的MAP均明显升高,且在SPE组中升高最为显著(均P<0.01);PE组的Pl GF和PAPP-A水平较NP组均明显降低(均P<0.01),h CG水平仅在SPE组中显著上升(P<0.01);PE组中PP13、s Flt-1、s Eng和VEGF水平与NP组比较,差别均无明显统计学意义。GH组的各项血清学指标水平较NP组均无明显差别;(3)Logistic回归示SPE的最佳预测模型为:母龄联合MAP、PAPP-A、Pl GF和HCG,AUC达0.924(95%CI:0.870~0.961;P<0.01),敏感度和特异度分别为94%和78%;Non-SPE的最佳预测模型为:BMI联合MAP、PAPP-A和Pl GF,AUC达0.877(95%CI:0.813~0.925;P<0.01),敏感度和特异度分别为86%和74%。结论中孕早期联合母体特征、MAP及母体血清学指标PAPP-A、Pl GF、h CG的水平变化能提高PE的检出率,且有助于评估其严重程度。
Objective To explore the value of maternal features, mean arterial pressure (MAP) and serum markers in predicting preeclampsia (PE) in early pregnancy. Methods The blood samples were collected prospectively from May 2014 to May 2015 in pregnant women undergoing Down’s screening. The pregnancy outcomes were tracked and 31 cases of severe preeclampsia (SPE), 31 cases of non-severe preeclampsia (Non-SPE) 29 cases, 35 cases of pregnancy-induced hypertension (GH) and 120 cases of normal pregnancy (NP). The maternal characteristics, MAP and 7 serological markers were compared between the disease group and the control group. The Logistic and ROC curves were used to predict the sensitivity and specificity of PE separately or in combination to construct the optimal PE model. Results (1) Compared with NP group, the BMI of non-SPE and GH groups increased significantly in SPE group (all P <0.01); (2) MAP in PE and GH groups increased significantly, (P <0.01). The levels of PlGF and PAPP-A in PE group were significantly lower than those in NP group (all P <0.01), h CG level increased only in SPE group (P <0.01) ). The levels of PP13, s Flt-1, s Eng and VEGF in PE group were not significantly different from those in NP group. (3) Logistic regression showed that the best predictive model of SPE was: combination of age and mother with MAP, PAPP-A, Pl GF and HCG, the AUC reached 0.924 (95 % CI: 0.870-0.961, P <0.01). The sensitivity and specificity were 94% and 78% respectively. The best prediction model of Non-SPE was BMI combined with MAP, PAPP- 95% CI: 0.813-0.925; P <0.01). The sensitivity and specificity were 86% and 74% respectively. Conclusion The changes of PAPP-A, PlGF and hCG in the first trimester of pregnancy may improve the detection rate of PE and help to evaluate the severity of the maternal characteristics.