Division of Maternal-Fetal Medicine (Mr Shulman and Drs Aviram and Melamed), Department of Obstetrics and Gynecology, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada.
Department of Medicine (Dr Shah), Institute for Clinical Evaluative Sciences, and Institute for Health Policy, Management, and Evaluation, Sunnybrook Research Institute, Ontario, Canada; Division of Endocrinology (Drs Shah and Retnakaran), Department of Obstetrics and Gynecology, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada.
Am J Obstet Gynecol MFM. 2023 Aug;5(8):101042. doi: 10.1016/j.ajogmf.2023.101042. Epub 2023 Jun 6.
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.
在患有糖尿病的妊娠中，产前检测胎儿生长加速和巨大儿对于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的最常用工具。然而，超声胎儿体重估测这些结果的预测准确性是有限的。此外，在分娩前通常无法获得最新的超声胎儿体重估计值。这可能导致无法识别巨大儿，尤其是在患有糖尿病的妊娠中，护理提供者可能低估胎儿生长速度。因此，需要更好的工具来检测并提醒护理提供者潜在的加速胎儿生长和巨大儿风险。
本研究旨在开发和验证患有糖尿病的妊娠中与出生体重和巨大儿相关的预测模型。
这是一项在 2011 年 1 月至 2022 年 5 月期间在一家三级中心观察到的患有糖尿病（既往或妊娠期）的单胎活产≥36 周妊娠的回顾性队列研究。候选预测因子包括母亲年龄、产次、糖尿病类型、最近的超声胎儿体重估计（包括估计胎儿体重、腹围 z 评分、头围-腹围 z 评分比和羊水）、胎儿性别以及超声检查与分娩之间的时间间隔。研究结局为巨大儿（定义为出生体重>4000g 和>4500g）、大于胎龄儿（定义为出生体重>胎龄的第 90 百分位数）和出生体重（以克为单位）。多变量逻辑回归模型用于估计二项结局的概率，多变量线性回归模型用于估计出生体重。计算模型的区分度和预测准确性。使用自举重采样技术进行内部验证。
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。婴儿出生体重>4000g、>4500g 和>胎龄第 90 百分位数的总体比例分别为 8%、1%和 12%。最具贡献的预测变量是估计胎儿体重、腹围 z 评分、超声检查至分娩的时间间隔和糖尿病类型。这 3 个二项结局模型具有较高的区分度（曲线下面积接收者操作特征曲线，0.929-0.979），高于单独使用估计胎儿体重的曲线下面积接收者操作特征曲线（0.880-0.931）。这些模型的预测准确性具有较高的敏感性（87%-100%）、特异性（84%-92%）和阴性预测值（84%-92%）。该模型预测出生体重的准确性具有较低的系统和随机误差（分别为 0.6%和 7.5%），明显小于单独使用估计胎儿体重的相应误差（分别为-5.9%和 10.8%）。实际出生体重的估计值在 5%、10%和 15%以内的比例较高（分别为 52.3%、82.9%和 94.9%）。
与当前包括估计胎儿体重在内的标准护理相比，当前研究中开发的预测模型在预测巨大儿、大于胎龄儿和出生体重方面具有更高的预测准确性。这些模型可能有助于护理提供者为患者提供关于最佳分娩时机和方式的咨询。
