Lin Qing, Fang Zhuan-Ji
Department of Obstetrics, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, Fujian Province, China.
World J Diabetes. 2023 Oct 15;14(10):1541-1550. doi: 10.4239/wjd.v14.i10.1541.
Gestational diabetes mellitus (GDM) is a condition characterized by high blood sugar levels during pregnancy. The prevalence of GDM is on the rise globally, and this trend is particularly evident in China, which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses. Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses. Therefore, this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin, blood glucose, and body mass index (BMI) on the occurrence of GDM.
To develop a risk prediction model to analyze factors leading to GDM, and evaluate its efficiency for early prevention.
The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed. According to whether GDM occurred, they were divided into two groups to analyze the related factors affecting GDM. Then, according to the weight of the relevant risk factors, the training set and the verification set were divided at a ratio of 7:3. Subsequently, a risk prediction model was established using logistic regression and random forest models, and the model was evaluated and verified.
Pre-pregnancy BMI, previous history of GDM or macrosomia, hypertension, hemoglobin (Hb) level, triglyceride level, family history of diabetes, serum ferritin, and fasting blood glucose levels during early pregnancy were de-termined. These factors were found to have a significant impact on the development of GDM ( < 0.05). According to the nomogram model's prediction of GDM in pregnancy, the area under the curve (AUC) was determined to be 0.883 [95% confidence interval (CI): 0.846-0.921], and the sensitivity and specificity were 74.1% and 87.6%, respectively. The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin, fasting blood glucose in early pregnancy, pre-pregnancy BMI, Hb level and triglyceride level. The random forest model achieved an AUC of 0.950 (95%CI: 0.927-0.973), the sensitivity was 84.8%, and the specificity was 91.4%. The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model ( < 0.05).
The random forest model is superior to the nomogram model in predicting the risk of GDM. This method is helpful for early diagnosis and appropriate intervention of GDM.
妊娠期糖尿病（GDM）是一种在孕期出现血糖水平升高的病症。全球范围内，GDM的患病率呈上升趋势，这一趋势在中国尤为明显，已成为影响准妈妈及其胎儿健康的重大问题。及时识别和处理GDM对于维持准妈妈及其发育中胎儿的健康至关重要。因此，本研究旨在建立GDM的风险预测模型，并探讨血清铁蛋白、血糖和体重指数（BMI）对GDM发生的影响。
建立一个风险预测模型，以分析导致GDM的因素，并评估其早期预防的有效性。
回顾性分析2020年4月至2022年12月在福建省妇幼保健院接受常规产前检查的406名孕妇的临床资料。根据是否发生GDM将她们分为两组，分析影响GDM的相关因素。然后，根据相关危险因素的权重，以7:3的比例划分训练集和验证集。随后，使用逻辑回归和随机森林模型建立风险预测模型，并对模型进行评估和验证。
确定了孕前BMI、既往GDM或巨大儿病史、高血压、血红蛋白（Hb）水平、甘油三酯水平、糖尿病家族史、血清铁蛋白以及孕早期空腹血糖水平。发现这些因素对GDM的发生有显著影响（<0.05）。根据列线图模型对孕期GDM的预测，曲线下面积（AUC）为0.883 [95%置信区间（CI）：0.846 - 0.921]，敏感性和特异性分别为74.1%和87.6%。随机森林模型中预测GDM发生的前五个变量是血清铁蛋白、孕早期空腹血糖、孕前BMI、Hb水平和甘油三酯水平。随机森林模型的AUC为0.950（95%CI：0.927 - 0.973），敏感性为84.8%，特异性为91.4%。德龙检验表明，随机森林模型的AUC值高于决策树模型（<0.05）。
在预测GDM风险方面，随机森林模型优于列线图模型。该方法有助于GDM的早期诊断和适当干预。
