孕中期超声估计胎儿体重与子痫前期风险的竞争风险模型分析

朱玲; 潘雪葵; 王会凤; 梅丽娜

doi:10.16766/j.cnki.issn.1674-4152.002995

孕中期超声估计胎儿体重与子痫前期风险的竞争风险模型分析

doi: 10.16766/j.cnki.issn.1674-4152.002995

湖州市妇幼保健院超声科, 浙江湖州 313000

基金项目:

浙江省医药卫生科技计划项目 2022KY1226

详细信息

通讯作者:
梅丽娜，E-mail：452161987@qq.com

中图分类号: R445.1 R714.51
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出版历程
- 收稿日期: 2022-02-23

Value of the competing risk model including sonographically estimated mid-gestation fetal weight in predicting pre-eclampsia risk

Department of Ultrasound, Huzhou Maternity and Child Health Care Hospital, Huzhou, Zhejiang 313000, China

摘要

摘要: 目的探讨孕中期超声估计胎儿体重构建子痫前期风险的竞争风险模型，并分析模型效能。方法选取2019年3月—2020年12月于湖州市妇幼保健院接受常规孕检的2 234例妊娠19⁺⁰至24⁺⁶周的妇女为研究对象。记录所有受试者人口统计学特征和病史，超声估计胎儿体重及血流动力学指标。随访至分娩，记录随访过程中患者子痫前期发生情况，并以发生子痫前期患者的基本资料为基础，使用1∶1倾向性匹配法匹配未发生组患者，采用竞争风险模型评估妊娠妇女子痫前期发生的独立危险因素，构建子痫前期竞争风险模型，并分析其预测效能。结果纳入的2 234例孕妇中失访63例，最终纳入2 171例，其中86例发生子痫前期，为发生组。经倾向性1∶1匹配共60对匹配成功。竞争风险的多因素分析显示，高平均动脉压(MAP)、外周血管阻力(PVR)、平均子宫动脉指数(UtA-PI)孕妇发生子痫前期风险显著升高(SHR=1.150、1.102、1.168，均P < 0.05)；超声估计高胎儿体重者发生子痫前期风险显著降低(SHR=0.110，P < 0.05)。根据竞争风险模型回归分析筛选的危险因素建立子痫前期发生风险预测列线图，C指数为0.785，一致性良好。结论高MAP、PVR、UtA-PI是子痫前期的危险因素，超声估计高胎儿体重是子痫前期的保护因素，基于此建立的子痫前期风险的竞争风险模型具有较好的预测能力及临床实用性，有助于对子痫前期高风险人群的筛查。
- 孕中期 /
- 超声 /
- 胎儿体重 /
- 子痫前期 /
- 竞争风险模型
Abstract: Objective To develop a competing risk model using sonographically estimated mid-gestation fetal weight for predicting pre-eclampsia risk, and to assess the effectiveness of the model. Methods A total of 2 234 women from 19⁺⁰ to 24⁺⁶ weeks of gestation who underwent routine antenatal checks at Huzhou Maternity and Child Health Care Hospital from March 2019 to December 2020 were selected for this study. Demographic characteristics and medical history were recorded, and fetal weight and hemodynamic parameters were estimated by sonography. The incidence of pre-eclampsia was determined by a follow-up till delivery. General data were compared between women with and without pre-eclampsia matched in a 1∶1 ratio using propensity score matching. A competing risk model was used to screen independent risk factors for pre-eclampsia, and the identified risk factors were used to construct a competing risk model for pre-eclampsia, and the predictive performance of the model was analyzed. Results Of the 2 234 cases, 63 were lost to follow-up and the remaining 2 171 were finally included, including 86 with pre-eclampsia (pre-eclampsia group). Sixty pairs were successfully matched. Multivariate competing risk analysis showed that the risk of pre-eclampsia was significantly increased in pregnant women with high mean arterial pressure (MAP), peripheral vascular resistance (PVR) and mean uterine artery pulsatility index (UtA-PI, SHR=1.150, 1.102 and 1.168, all P < 0.05), whereas the risk was significantly lower in those with high fetal weight estimated using sonography (SHR=0.110, P < 0.05). The nomogram constructed using risk factors identified by competing-risks regression obtained a C-index of 0.785 in predicting the risk of pre-eclampsia, showing a high concordance between prediction and reality. Conclusion High MAP, PVR and UtA-PI are risk factors for pre-eclampsia, whereas high fetal weight at mid-gestation stage estimated by sonography is a protective factor for pre-eclampsia. The competing risk model for predicting the risk of pre-eclampsia by including the four aforementioned factors has a high predictive value and clinical practicality, which will facilitate screening for pre-eclampsia in high-risk groups.
- Mid-gestation stage /
- sonography /
- Fetal weight /
- Pre-eclampsia /
- Competitive risk model
利益冲突 无

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图 1 子痫前期发生风险预测列线图模型

Figure 1. Nomograms model for predicting the risk of preeclampsia

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图 2 内部校正曲线图

Figure 2. Internal calibration curve diagram

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表 1 倾向性匹配后2组孕妇年龄、孕周、产次及BMI比较

Table 1. Comparison of age, gestational age, parity, and BMI between the two groups after propensity matching

组别	例数	年龄(x±s，岁)	孕周(x±s，周)	产次(例)		BMI(x±s)
组别	例数	年龄(x±s，岁)	孕周(x±s，周)	≤2次	> 2次	BMI(x±s)
发生组	60	25.32±3.45	21.42±1.47	45	15	26.92±3.88
未发生组	60	26.74±4.41	21.87±1.34	39	21	25.78±2.43
统计量		1.964^a	1.752^a	1.429^b		1.929^a
P值		0.052	0.082	0.232		0.057
注：^a为t值，^b为χ²值。

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表 2 2组孕妇一般资料比较

Table 2. Comparison of general information of pregnant women in two groups

组别	例数	吸烟[例(%)]	喝酒[例(%)]	高血压家族史[例(%)]	心率(x±s，次/min)	MAP(x±s，mmHg)	CO(x±s，L/min)	PVR(x±s，dynes/s)	UtA-PI(x±s)
发生组	60	5(8.33)	6(10.00)	21(35.00)	86.58±7.80	94.34±10.26	5.18±0.91	1 479.47±133.42	0.96±0.28
未发生组	60	9(15.00)	2(3.33)	12(20.00)	84.26±8.86	90.75±9.30	5.32±0.83	1 410.18±131.97	0.85±0.25
统计量		1.294^a	1.205^a	3.386^a	1.522^b	2.008^b	0.880^b	2.860^b	2.270^b
P值		0.255	0.272	0.066	0.131	0.047	0.380	0.005	0.025

组别	例数	超声估计胎儿体重[M(P₂₅, P₇₅)，kg]		谷丙转氨酶(x±s，mg/L)	尿酸(x±s，μmol/L)		谷草转氨酶(x±s，mmol/L)	血清肌酐(x±s，μmol/L)	HCG(x±s，IU/mL)
发生组	60	0.52(0.42，0.66)		37.83±5.41	225.98±54.48		33.30±2.53	65.56±12.91	35.00±8.39
未发生组	60	0.67(0.57, 0.75)		36.26±4.32	218.54±50.96		32.56±2.22	62.83±19.80	33.60±8.25
统计量		2.921^c		1.757^b	0.773^b		1.703^b	0.895^b	0.922^b
P值		0.004		0.082	0.441		0.091	0.373	0.359
注：^a为χ²值，^b为t值，^c为U值。

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表 3 子痫前期发生风险的竞争风险模型回归分析

Table 3. Regression analysis of competing risk models for the risk of developing preeclampsia

变量	B	SE	Wald χ²	P值	SHR	95% CI
MAP	0.048	0.022	4.676	0.031	1.150	1.105~1.197
PVR	0.006	0.002	10.693	0.001	1.106	1.102~1.109
UtA-PI	2.586	1.090	5.629	0.018	1.282	1.168~1.513
超声估计胎儿体重	-4.597	1.489	9.529	0.002	0.110	0.001~0.187

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