Abstract：Objective To explore the factors affecting the onset of gestational diabetes mellitus (GDM), and to establish and validate the interactive nomogram model for the prediction of risk of GDM. Methods We retrospectively collected the clinical data about 2,279 pregnant women admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January to December 2021, and the dataset was randomly divided into the training set (80%) and the validation set (20%). χ2 test and Wilcoxon rank sum test were used to analyze the factors affecting the risk of onset of GDM. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was performed to select the predictive variables for the establishment of interactive nomogram model, and then the model was validated. Receiver operating characteristic curve (ROC), calibration curve, clinical decision curve analysis (DCA) and clinical impact curve (CIC) were employed to evaluate the performance of the model. Results A total of 1,823 pregnant womenwere enrolled into the training set,and the prevalence rate of GDM was 16.84%. Multivariate LASSO logistic regression analysis was conducted to identify the predictors of risk of GDM, among which pregnancy age (OR=1.094, 95%CI:1.055-1.135), pre-pregnancy BMI (OR=1.177, 95%CI:1.126-1.230), rural registered residence (OR=0.242, 95%CI:0.073-0.805), bachelor degree or above (OR=2.003, 95%CI:1.291-3.106), history of diabetes mellitus (OR=6.516, 95%CI:4.034-10.525), primiparity (OR=0.600, 95%CI:0.430-0.837) and abortion (OR=1.905, 95%CI:1.642-2.704) were independently associated with GDM. The interactive nomogram model was constructed based on the above-mentioned factors, and the area under ROC (95% confidence interval) in the training set and the validation set was 0.747 (0.717-0.777) and 0.741 (0.679-0.802) respectively. Calibration curve and CIC revealed that the values predicted by the established model were in good agreement with the actual values. DCA indicated that the maximum net benefit value would be achieved by the nomogram modelwhen the threshold probability intervals for GDM were 0.10-0.65. Conclusion The nomogram predictive model for GDM has higher discrimination, calibration and clinical applicability, and is conducive to early identifying the high-risk population for GDM and taking preventive measures.
李娜娜, 张师静, 栗浩然, 陈巧敏, 王雅莉. 妊娠糖尿病发病风险交互式列线图预测模型的构建和验证[J]. 实用预防医学, 2024, 31(5): 574-579.
LI Nana, ZHANG Shijing, LI Haoran, CHEN Qiaomin, WANG Yali. Establishment and validation of interactive nomogram model for predicting the risk of gestational diabetes mellitus. , 2024, 31(5): 574-579.
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