Construction and validation of a line chart for gestational diabetes mellitus based on clinical indicators

Background Gestational diabetes mellitus (GDM) is a common complication of mid-to-late pregnancy. Here, we constructed a predictive model for GDM based on a combination of clinical characteristics and relevant serum markers. Methods Data from full-term singleton vaginal deliveries from January 2022 to January 2023 were retrospectively collected from the obstetrics department. The data collected were segregated and assigned to training, validation, and external test sets. Maternal demographic characteristics, living and working habits, and haematological indicators, such as liver function and lipids were collected using a questionnaire designed for the study. The "rms" package in R was used to explore GDM-associated factors through stepwise regression at P < 0.05. A predictive model was developed based on the results of multifactorial logistic regression analysis. We then evaluated the differentiation of the column-line graphical model and performed internal and external validation. To assess the accuracy of the bar graphical model, we plotted calibration and decision curves. Results Data from 265 pregnant women were included in the training and internal validation sets, and data from 113 pregnant women were included in the external validation set. The logistic regression algorithm screened 8 indicators as predictors. A prediction model was constructed with ALT, TBA, TC, and TG levels while considering whether GDM affects appetite, the husband- wife relationship, family history, and parental relationships as predictors. The Hosmer-Lemeshow goodness-of-fit test revealed that the chi-square values for the modelling, internal validation, and external validation groups (chi(2) = 5.964, 3.249, and 12.182, respectively) were all P > 0.05. The ROC curve AUCs for the three groups were 0.93 (95% CI: 0.89-0.97), 0.72 (95% CI: 0.62-0.81), and 0.68 (95% CI: 0.53-0.83), respectively. Conclusion In this study, a GDM prediction model was constructed to achieve high performance in GDM risk prediction based on routine obstetric tests and information.