Development and validation of nomogram for the prediction of preterm delivery based on patient characteristics and circulating inflammatory cells in patients with gestational diabetes mellitus

Abstract

Background The incidence of preterm delivery (<37 weeks’ gestation) is increased due to gestational diabetes mellitus (GDM). The preterm delivery is the leading cause of death in children. If potential preterm delivery can be diagnosed early and then prevented, adverse pregnancy outcomes can be improved. Therefore, effective methods are needed for early prediction of preterm delivery in women with GDM. Methods Patients with GDM defined as the presence of at least 1 plasma glucose abnormality at 24–28 weeks of pregnancy [fasting plasma glucose ≥5.1 mmol/L, 60-min ≥10.0 mmol/L, 120-min ≥8.5 mmol/L by 75 g oral glucose tolerance test (OGTT)] from the First Affiliated Hospital of Wenzhou Medical University were enrolled. The data (564 patients) recorded from January 2017 to June 2020 were named the training cohort, and the data (242 patients) obtained from patients with GDM, from July 2020 to January 2022, were named the validation cohort. Mann-Whitney U test and chi-square test were used to compare the skewed distributed and categorical data, respectively. According to the results of univariate logistic regression analysis, the multivariate logistic regression model was developed in the training cohort. Then, the nomogram was established. The validation of the nomogram was conducted on the training and validation cohort. Results No significant differences in baseline characteristics were detected between the 2 cohorts (all P>0.05). The multivariate analysis suggested that maternal age, insulin use, NLR, and monocyte count were the independent predictors of preterm delivery. A nomogram for predicting the probability of preterm delivery was developed. The model suggested good discrimination [areas under the curve (AUC) =0.885, 95% confidence interval (95% CI): 0.855–0.910, sensitivity =83.0%, specificity =83.1% in the training cohort; AUC =0.919, 95% CI: 0.858–0.980, sensitivity =90.6%, specificity =84.8% in the validation cohort] and good calibration [Hosmer-Lemeshow (HL) test: χ 2 =3.618, P=0.306 in the training cohort; χ 2 =6.012, P=0.111 in the validation cohort]. Conclusions The visual nomogram model appears to be a reliable approach for the prediction of preterm delivery, allowing clinicians to take timely measures to prevent the occurrence of preterm delivery in women with GDM at the time of GDM diagnosis, and deserves further investigation.