Li Ruiyan, Yuan Kun, Yu Xiaoyun, Jiang Yan, Liu Ping, Zhang Kuiwei
Department of Obstetrics, Xi'an International Medical Center Hospital No. 777 Xitai Road, High Tech Zone, Xi'an 710100, Shaanxi, China.
High Risk Obstetrics Department II, Gansu Provincial Maternity and Child-care Hospital No. 143 Qilihe North Street, Qilihe District, Lanzhou 730050, Gansu, China.
Am J Transl Res. 2023 Feb 15;15(2):1223-1230. eCollection 2023.
To construct a model to predict the risk of gestational diabetes mellitus (GDM) based on a nomogram and verify it.
Data from 182 patients with GDM treated in Xi'an International Medical Center Hospital from January 2018 to May 2021 were retrospectively analyzed. A total of 491 normal parturients who underwent physical examination in Xi'an International Medical Center Hospital during the same period were selected as controls. With a ratio of 7:3, patients with GDM were divided into a training group (n=128) and a verification (n=54) group, and 491 normal parturients were divided into a training control group (n=344) and a verification control group (n=147). Clinical data were collected, and risk factors for GDM were analyzed by logistic regression. R language was used to construct a prognostic prediction nomogram model for GDM, and a receiver operating characteristic curve was employed to evaluate the accuracy of this nomogram model in predicting the prognosis of GDM.
Univariate analysis revealed that age, body mass index (BMI), family history of diabetes, hemoglobin, triglycerides, serum ferritin, and fasting blood glucose in the first trimester were different between the training group and the training control group (P<0.05). Multivariate analysis revealed that age, BMI, hemoglobin, triglycerides, serum ferritin, and fasting blood glucose in the first trimester were independent risk factors for GDM (P<0.05). Based on a logistic regression equation, the risk formula was -5.971 + 1.054 * age + 1.133 * BMI + 1.763 * hemoglobin + 1.260 * triglycerides + 3.041 * serum ferritin + 1.756 * fasting blood glucose in the first trimester. The area under the curve for predicting the risk of GDM in the training group was 0.920, and that of the validation group was 0.753.
Age, BMI, hemoglobin, serum ferritin, and fasting blood glucose in the first trimester are risk factors for GDM.
构建基于列线图预测妊娠期糖尿病（GDM）风险的模型并进行验证。
回顾性分析2018年1月至2021年5月在西安国际医学中心医院治疗的182例GDM患者的数据。同期选取在西安国际医学中心医院进行体检的491例正常产妇作为对照。按照7:3的比例，将GDM患者分为训练组（n = 128）和验证组（n = 54），将491例正常产妇分为训练对照组（n = 344）和验证对照组（n = 147）。收集临床数据，通过逻辑回归分析GDM的危险因素。使用R语言构建GDM的预后预测列线图模型，并采用受试者工作特征曲线评估该列线图模型预测GDM预后的准确性。
单因素分析显示，训练组与训练对照组在年龄、体重指数（BMI）、糖尿病家族史、血红蛋白、甘油三酯、血清铁蛋白及孕早期空腹血糖方面存在差异（P < 0.05）。多因素分析显示，年龄、BMI、血红蛋白、甘油三酯、血清铁蛋白及孕早期空腹血糖是GDM的独立危险因素（P < 0.05）。基于逻辑回归方程，风险公式为 -5.971 + 1.054×年龄 + 1.133×BMI + 1.763×血红蛋白 + 1.260×甘油三酯 + 3.041×血清铁蛋白 + 1.756×孕早期空腹血糖。训练组预测GDM风险的曲线下面积为0.920，验证组为0.753。
年龄、BMI、血红蛋白、血清铁蛋白及孕早期空腹血糖是GDM的危险因素。
