妊娠糖尿病孕妇产后抑郁发生风险的列线图模型构建及初步验证

朱丹; 张丽芹; 张海波; 朱玲玲; 陈茜娅

doi:10.16766/j.cnki.issn.1674-4152.003906

妊娠糖尿病孕妇产后抑郁发生风险的列线图模型构建及初步验证

doi: 10.16766/j.cnki.issn.1674-4152.003906

南通大学附属南通妇幼保健院产科，江苏南通 226018

基金项目:

江苏省卫生健康委科研课题 ZD2021036

南通市自然科学基金和社会民生科技计划(指导性)项目 MSZ2023014

详细信息

通讯作者:
张丽芹，E-mail：1951456813@qq.com

中图分类号: R714.256 R749.4
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出版历程
- 收稿日期: 2024-11-08
- 网络出版日期: 2025-05-14

Construction and preliminary validation of a nomogram model for predicting the risk of postpartum depression in gestational diabetes mellitus patients

Department of Obstetrics, Nantong Maternal and Child Health Hospital Affiliated to Nantong University, Nantong, Jiangsu 226018, China

摘要

摘要: 目的构建妊娠糖尿病(GDM)孕妇产后抑郁(PPD)发生风险的预测列线图模型，并对其预测效能进行初步验证。方法回顾性纳入南通大学附属南通妇幼保健院2021年1月—2023年12月收治的318例GDM孕妇作为研究对象，对孕妇于分娩后6周采用爱丁堡产后抑郁量表(EPDS)进行评估，根据评估结果将患者分为PPD组76例(23.9%)和非PPD组242例(76.1%)。收集2组人口学资料、妊娠期临床特征、血糖指标、并发症发生情况等。采用多因素logistic回归分析筛选PPD发生的独立危险因素，并基于回归结果构建列线图模型。采用ROC曲线和校准曲线对模型的区分度和校准度进行评价，同时绘制临床决策曲线。结果多因素logistic回归分析显示，孕前BMI、妊娠期体重增长量、独生子女、家庭人均月收入、空腹血糖、餐后2小时血糖以及妊娠期并发症发生均是GDM孕妇PPD发生的独立影响因素(P＜0.05)。基于上述变量构建列线图模型，其ROC曲线下面积为0.958(95% CI: 0.893~0.989)，模型具有良好的区分度；模型的校准度良好，预测值与实际观测值基本一致(Hosmer & Lemeshow χ²=5.399，P=0.714)；临床决策曲线结果显示，利用该模型预测GDM患者发生PPD的风险>0.3时，及时针对患者临床特点进行干预，可以获得较高的净获益。结论本研究构建的GDM孕妇PPD发生风险预测列线图模型简单易用，预测效能良好，可为临床早期识别和干预GDM孕妇PPD提供参考依据。
- 妊娠糖尿病 /
- 产后抑郁 /
- 列线图 /
- 风险预测 /
- 模型构建
Abstract: Objective To construct a nomogram model for predicting the risk of postpartum depression (PPD) in women with gestational diabetes mellitus (GDM) and to preliminarily validate its predictive efficacy. Methods A total of 318 GDM patients admitted to Nantong Maternal and Child Health Care Hospital from January 2021 to December 2023 were retrospectively enrolled in this study. Six weeks after delivery, the Edinburgh postnatal depression scale (EPDS) was used to assess PPD, and the patients were divided into a PPD group (n=76, 23.9%) and a non-PPD group (n=242, 76.1%). Demographic characteristics, gestational clinical features, blood glucose levels, and complications were collected. Independent risk factors for PPD were screened by multivariate logistic regression analysis, and a nomogram model was constructed based on the regression results. The discrimination and calibration of the model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve, respectively. A clinical decision curve was also plotted. Results Multivariate logistic regression analysis showed that pre-pregnancy BMI, gestational weight gain, being an only child, average monthly family income, fasting blood glucose, 2-hour postprandial blood glucose, and the occurrence of gestational complications were independent risk factors for PPD in GDM patients (P < 0.05). The nomogram model based on these variables had an area under the ROC curve of 0.958 (95% CI: 0.893-0.989), indicating good discrimination. The model also showed good calibration, with the predicted values being consistent with the actual observed values (Hosmer & Lemeshow χ²=5.399, P=0.714). The clinical decision curve showed that when the risk of PPD in GDM patients predicted by this model was >0.3, timely intervention based on the patient's clinical characteristics could achieve higher net benefits. Conclusion The nomogram model for predicting the risk of PPD in GDM patients constructed in this study is simple, easy to use, and has good predictive efficacy. It can provide a reference for the early identification and intervention of PPD in GDM patients in clinical practice.
- Gestational diabetes mellitus /
- Postpartum depression /
- Nomogram /
- Risk prediction /
- Model construction

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图 1 预测GDM孕妇产后抑郁发生风险的列线图模型

Figure 1. Nomogram for a risk prediction model for postpartum depression in women with GDM

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图 2 列线图模型的校准曲线和ROC曲线

Figure 2. Calibration and ROC curves of the nomogram model

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图 3 列线图模型的临床决策曲线

Figure 3. The clinical decision curve for a nomogram model

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表 1 2组GDM孕妇基本情况比较

Table 1. Comparison of baseline characteristics between two groups of pregnant women with GDM

项目	PPD组(n=76)	非PPD组(n=242)	统计量	P值
年龄(x±s，岁)	31.5±4.8	31.0±4.4	0.845^a	0.398
文化程度[例(%)]			0.058^b	0.954
初中及以下	9(11.8)	25(10.3)
高中/中专	22(28.9)	76(31.4)
大专	27(35.5)	82(33.9)
本科及以上	18(23.7)	59(24.4)
职业[例(%)]			0.220^c	0.974
企业职工	29(38.2)	90(37.2)
事业单位人员	19(25.0)	67(27.7)
自由职业者	14(18.4)	43(17.8)
无业	14(18.4)	42(17.3)
家庭人均月收入[例(%)]			3.135^b	＜0.001
＜3 000元	31(40.8)	45(18.6)
3 000~5 000元	29(38.2)	128(52.9)
>5 000元	16(21.0)	69(28.5)
居住地[例(%)]			0.208^c	0.648
城市	50(65.8)	166(68.6)
农村	26(34.2)	76(31.4)
独生子女[例(%)]			16.530^c	＜0.001
是	38(50.0)	61(25.2)
否	38(50.0)	181(74.8)
孕次[M(P₂₅, P₇₅，次]	2(1, 3)	2(1, 2)	0.202^b	0.840
产次[M(P₂₅, P₇₅)，次]	1(1, 2)	1(1, 2)	0.748^b	0.454
注：^a为t值，^b为Z值，^c为χ²值。

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表 2 2组GDM孕妇妊娠期及分娩情况比较

Table 2. Comparison of conditions during Pregnancy and delivery between the two groups of women with GDM

项目	PPD组(n=76)	非PPD组(n=242)	统计量	P值
孕前BMI(x±s)	26.13±3.51	24.87±3.04	3.034^a	0.003
妊娠期体重增长(x±s，kg)	15.24±4.38	14.05±3.87	2.264^a	0.024
GDM诊断孕周(x±s，周)	29.31±4.92	28.25±4.68	1.701^a	0.090
FBG(x±s，mmol/L)	6.61±0.89	5.29±0.72	13.143^a	＜0.001
2hPG(x±s，mmol/L)	7.48±1.17	7.01±1.03	3.357^a	＜0.001
妊娠期并发症发生[例(%)]	14(18.4)	17(7.0)	8.511^b	0.003
分娩方式[例(%)]			2.077^b	0.149
阴道分娩	42(55.3)	156(64.5)
剖宫产	34(44.7)	86(35.5)
新生儿出生体重(x±s，g)	3 245.87±456.21	3 187.65±421.98	1.029^a	0.304
新生儿Apgar评分(x±s，分)	8.89±0.98	8.95±0.76	0.558^a	0.577
注：^a为t值，^b为χ²值。

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表 3 GDM孕妇PPD影响因素的logistic回归分析

Table 3. Logistic regression analysis of influencing factors for PPD in pregnant women with GDM

变量	B	SE	Waldχ²	P值	OR值	95% CI
孕前BMI	0.379	0.121	9.812	0.002	1.461	1.187~1.801
妊娠期体重增长	0.715	0.242	8.725	0.003	2.045	1.332~3.142
FBG	1.021	0.315	10.421	0.001	2.776	1.503~5.127
2hPG	1.287	0.279	21.139	＜0.001	3.625	2.258~5.819
妊娠期并发症	1.982	0.697	8.086	0.004	7.257	1.851~28.449
家庭人均月收入＜3 000元	0.853	0.285	8.974	0.003	2.346	1.375~4.008
独生子女	0.665	0.212	9.795	0.002	1.945	1.269~2.987

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