Department of Chemical Pathology, Benue State University, Makurdi, Nigeria.
Department of Chemical Pathology, Nile University of Nigeria, Abuja, Nigeria.
BMC Pregnancy Childbirth. 2024 May 6;24(1):346. doi: 10.1186/s12884-024-06519-7.
The implementation of universal screening for Gestational Diabetes Mellitus (GDM) is challenged by several factors key amongst which is limited resources, hence the continued reliance on risk factor-based screening. Effective identification of high-risk women early in pregnancy may enable preventive intervention. This study aimed at developing a GDM prediction model based on maternal clinical risk factors that are easily assessable in the first trimester of pregnancy in a population of Nigerian women.
This was a multi-hospital prospective observational cohort study of 253 consecutively selected pregnant women from which maternal clinical data was collected at 8-12 weeks gestational age. Diagnosis of GDM was made via a one-step 75-gram Oral Glucose Tolerance Test (OGTT) at 24-28 weeks of gestation. A GDM prediction model and nomogram based on selected maternal clinical risk factors was developed using multiple logistic regression analysis, and its performance was assessed by Receiver Operator Curve (ROC) analysis. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 25 and Python programming language (version 3.0).
Increasing maternal age, higher body mass index (BMI), a family history of diabetes mellitus in first-degree relative and previous history of foetal macrosomia were the major predictors of GDM. The model equation was: LogitP = 6.358 - 0.066 × Age - 0.075 × First trimester BMI - 1.879 × First-degree relative with diabetes mellitus - 0.522 × History of foetal macrosomia. It had an area under the receiver operator characteristic (ROC) curve (AUC) of 0.814 (95% CI: 0.751-0.877; p-value < 0.001), and at a predicted probability threshold of 0.745, it had a sensitivity of 79.2% and specificity of 74.5%.
This first trimester prediction model reliably identifies women at high risk for GDM development in the first trimester, and the nomogram enhances its practical applicability, contributing to improved clinical outcomes in the study population.
实施妊娠期糖尿病（GDM）的普遍筛查受到多种因素的挑战，其中关键因素是资源有限，因此仍然依赖于基于危险因素的筛查。在妊娠早期有效识别高危妇女可能会进行预防性干预。本研究旨在建立一种基于尼日利亚妇女妊娠早期易评估的母体临床危险因素的 GDM 预测模型。
这是一项多医院前瞻性观察队列研究，共纳入 253 名连续选择的孕妇，在妊娠 8-12 周时收集母体临床数据。通过 24-28 周妊娠的一步 75 克口服葡萄糖耐量试验（OGTT）诊断 GDM。使用多因素逻辑回归分析建立基于选定母体临床危险因素的 GDM 预测模型和诺模图，并通过接受者操作特征曲线（ROC）分析评估其性能。数据分析使用社会科学统计软件包（SPSS）第 25 版和 Python 编程语言（第 3.0 版）进行。
母亲年龄增长、更高的体重指数（BMI）、一级亲属的糖尿病家族史和以前的巨大儿史是 GDM 的主要预测因素。模型方程为：LogitP=6.358-0.066×年龄-0.075×初诊 BMI-1.879×一级亲属有糖尿病-0.522×有巨大儿史。它的受试者工作特征（ROC）曲线下面积（AUC）为 0.814（95%CI：0.751-0.877；p 值<0.001），在预测概率阈值为 0.745 时，它的灵敏度为 79.2%，特异性为 74.5%。
本研究建立的这种基于初诊的预测模型可以可靠地识别出妊娠早期发生 GDM 风险较高的女性，而诺模图增强了其实用性，有助于改善研究人群的临床结局。
