背景:产前检测妊娠合并糖尿病的胎儿生长加速和巨大儿对患者咨询和管理很重要。超声胎儿体重估算是预测出生体重和巨大儿最常用的工具。然而,超声胎儿体重估算对这些结局的预测准确性有限.此外,出生前通常无法获得最新的超声检查胎儿体重估算。这可能导致无法识别巨大儿,尤其是在合并糖尿病的孕妇中,护理提供者可能会低估胎儿的生长速度。因此,需要更好的工具来检测和提醒护理提供者注意胎儿生长加速和巨大儿的潜在风险.
目的:本研究旨在开发和验证妊娠合并糖尿病的出生体重和巨大儿的预测模型。
方法:这是一项完整的回顾性队列研究,研究对象是2011年1月至2022年5月期间在一个三级中心观察到的所有妊娠≥36周的单胎活产合并既往糖尿病或妊娠期糖尿病患者。候选预测因子包括产妇年龄,奇偶校验,糖尿病的类型,来自最新超声胎儿体重估计的信息(包括估计的胎儿体重,腹围z评分,头围与腹围z评分比,和羊水),胎儿性别,以及超声检查和出生之间的间隔。研究结果是巨大儿(定义为出生体重>4000和>4500克),胎龄大(定义为出生体重>胎龄的第90百分位数),和出生体重(克)。多变量逻辑回归模型用于估计二分结果的概率,多变量线性回归模型用于估计出生体重。计算模型判别和预测精度。使用引导重采样技术进行内部验证。
结果:共有2465名患者符合研究标准。大多数患者患有妊娠期糖尿病(90%),6%的患者患有2型糖尿病,4%的患者患有1型糖尿病。出生体重>4000g的婴儿的总比例,>4500克,胎龄>90百分位数为8%,1%,12%,分别。最有贡献的预测变量是估计的胎儿体重,腹围z评分,超声检查到出生间隔,和糖尿病的类型。3个二分结果的模型具有很高的判别准确性(曲线下面积接收器工作特性曲线,0.929-0.979),高于仅使用估计的胎儿体重(曲线下面积接收器工作特征曲线,0.880-0.931)。模型的预测精度具有较高的灵敏度(87%-100%),特异性(84%-92%),和阴性预测值(84%-92%)。模型对出生体重的预测精度具有较低的系统误差和随机误差(0.6%和7.5%,分别),大大小于仅使用估计胎儿体重(-5.9%和10.8%,分别)。估计比例在5%以内,10%,实际出生体重的15%很高(52.3%,82.9%,94.9%,分别)。
结论:当前研究中开发的预测模型与巨大儿的更高预测准确性相关,大的胎龄,与出生体重相比,目前的护理标准包括仅估计的胎儿体重。这些模型可以帮助护理提供者就最佳的分娩时机和方式为患者提供咨询。
Antenatal detection of accelerated fetal growth and macrosomia in pregnancies complicated by diabetes mellitus is important for patient counseling and management. Sonographic fetal weight estimation is the most commonly used tool to predict birthweight and macrosomia. However, the predictive accuracy of sonographic fetal weight estimation for these outcomes is limited. In addition, an up-to-date sonographic fetal weight estimation is often unavailable before birth. This may result in a failure to identify macrosomia, especially in pregnancies complicated by diabetes mellitus where care providers might underestimate fetal growth rate. Therefore, there is a need for better tools to detect and alert care providers to the potential risk of accelerated fetal growth and macrosomia.
This study aimed to develop and validate prediction models for birthweight and macrosomia in pregnancies complicated by diabetes mellitus.
This was a completed retrospective cohort study of all patients with a singleton live birth at ≥36 weeks of gestation complicated by preexisting or gestational diabetes mellitus observed at a single tertiary center between January 2011 and May 2022. Candidate predictors included maternal age, parity, type of diabetes mellitus, information from the most recent sonographic fetal weight estimation (including estimated fetal weight, abdominal circumference z score, head circumference-to-abdomen circumference z score ratio, and amniotic fluid), fetal sex, and the interval between ultrasound examination and birth. The study outcomes were macrosomia (defined as birthweights >4000 and >4500 g),
large for gestational age (defined as a birthweight >90th percentile for gestational age), and birthweight (in grams). Multivariable logistic regression models were used to estimate the probability of dichotomous outcomes, and multivariable linear regression models were used to estimate birthweight. Model discrimination and predictive accuracy were calculated. Internal validation was performed using the bootstrap resampling technique.
A total of 2465 patients met the study criteria. Most patients had gestational diabetes mellitus (90%), 6% of patients had type 2 diabetes mellitus, and 4% of patients had type 1 diabetes mellitus. The overall proportions of infants with birthweights >4000 g, >4500 g, and >90th percentile for gestational age were 8%, 1%, and 12%, respectively. The most contributory predictor variables were estimated fetal weight, abdominal circumference z score, ultrasound examination to birth interval, and type of diabetes mellitus. The models for the 3 dichotomous outcomes had high discriminative accuracy (area under the curve receiver operating characteristic curve, 0.929-0.979), which was higher than that achieved with estimated fetal weight alone (area under the curve receiver operating characteristic curve, 0.880-0.931). The predictive accuracy of the models had high sensitivity (87%-100%), specificity (84%-92%), and negative predictive values (84%-92%). The predictive accuracy of the model for birthweight had low systematic and random errors (0.6% and 7.5%, respectively), which were considerably smaller than the corresponding errors achieved with estimated fetal weight alone (-5.9% and 10.8%, respectively). The proportions of estimates within 5%, 10%, and 15% of the actual birthweight were high (52.3%, 82.9%, and 94.9%, respectively).
The prediction models developed in the current study were associated with greater predictive accuracy for macrosomia,
large for gestational age, and birthweight than the current standard of care that includes estimated fetal weight alone. These models may assist care providers in counseling patients regarding the optimal timing and mode of delivery.