INTERGROWTH-21st标准评价胎儿宫内生长受限的临床价值

 目的  探讨INTERGROWTH 21st标准评价胎儿宫内生长受限的应用价值。方法  本研究为前瞻性队列研究。收集2015年3月至2016年3月在复旦大学附属妇产科医院建卡,进行常规产前检查并入院分娩的单胎妊娠孕妇。所有入选病例孕28周之后入组,每4周做一次产前超声生长测量,直至分娩前。胎儿生长径线以INTERGROWTH 21st标准计算Z score值。分娩后随访新生儿体重等相关信息。应用Logistic回归方程建立预测公式。最后将新公式与Hadlock法预测IUGR的准确性进行对比。结果  共收集符合入组条件的孕妇834例。排除失访143例后,得到对照组565例和IUGR组126例。新预测公式的敏感性、特异性、阳性预测值、阴性预测值、假阳性率、假阴性率分别为:88.9%、85.4%、57.7%、97.2%、14.6%、11.1%。Hadlock法预测的敏感性、特异性、阳性预测值、阴性预测值、假阳性率、假阴性率分别为:81.7%、82.7%、51.2%、95.3%、17.3%、18.3%。结论  以INTERGROWTH-21st标准计算Z-score建立新公式预测胎儿宫内生长受限的方法,与传统的Hadlock预测法相比,改善了产前诊断的敏感性和特异度,减少了假阳性和假阴性的病例,全面提高了超声预测胎儿宫内生长受限的准确性。

The clinical value of INTERGROWTH-21st standard inevaluating intrauterine growth restriction

Objective  To explore the application value of INTERGROWTH-21st standard evaluate intrauterine growth restriction (IUGR).We aimed at predicting IUGR with ultrasonic fetal growth measurements.Methods  A prospective cohort of singleton pregnancy scanned after 28 gestational weeks was constructed.The Z score values of growth measurements were calculated using the INTERGROWTH-21st standard.Logistic regression equation was used to establish the prediction formula.Finally,the new formula was compared with the traditional Hadlock method to predict the accuracy of IUGR.We assessed outcomes for consenting participants who attended research scans and delivered at the Obstetrics and Gynecology Hospital of Fudan University.Results  A total  of 834 patients fulfilling the inclusion criteria were included.After the exclusion of 143 lost cases,565 of control cases and 126 of IUGR cases were obtained.The sensitivity,specificity,positive predictive value,negative predictive value,false positive rate and false negative rate of prediction model were respectively:88.9%,85.4%,57.7%,97.2%,14.6%,11.1%.In contrast,the values of Hadlock were respectively: 81.7%,82.7%,51.2%,95.3%,17.3%,18.3%.Conclusions  This study shows a prediction model of IUGR.INTERGROWTH-21st standard improved the diagnostic accuracy of IUGR,especially enhanced the positive predictive value.