Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study

Li, Luman; Zhu, Quing; Wang, Zihan; Yun, Tae-Jin; Liu, Huanyu; Tang, Fei; Liu, Song-Mei; Zhang, Yuanzhen

doi:10.3389/fendo.2023.1087994

Cited by 4 publications

(1 citation statement)

References 46 publications

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“…In recent years, the number of studies aimed at developing risk prediction models for GDM has increased along with the increased incidence of GDM. However, most of them used well-known risk factors for GDM as predictors of the models (e.g., age and prepregnancy body mass index) (29)(30)(31). This may result in the ineffective use of GDM-related serological indicators.…”

Section: Discussionmentioning

confidence: 99%

Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population

Zhang,

Dai,

Zhang

et al. 2023

Front. Endocrinol.

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Gestational diabetes mellitus (GDM) is one of the most common complications in pregnancy, impairing both maternal and fetal health in short and long term. As early interventions are considered desirable to prevent GDM, this study aims to develop a simple-to-use nomogram based on multiple common risk factors from electronic medical health records (EMHRs). A total of 924 pregnant women whose EMHRs were available at Peking University International Hospital from January 2022 to October 2022 were included. Clinical demographics and routine laboratory analysis parameters at 8-12 weeks of gestation were collected. A novel nomogram was established based on the outcomes of multivariate logistic regression. The nomogram demonstrated powerful discrimination (the area under the receiver operating characteristic curve = 0.7542), acceptable agreement (Hosmer-Lemeshow test, P = 0.3214) and favorable clinical utility. The C-statistics of 10-Fold cross validation, Leave one out cross validation and Bootstrap were 0.7411, 0.7357 and 0.7318, respectively, indicating the stability of the nomogram. A novel nomogram based on easily-accessible parameters was developed to predict GDM in early pregnancy, which may provide a paradigm for repurposing clinical data and benefit the clinical management of GDM. There is a need for prospective multi-center studies to validate the nomogram before employing the nomogram in real-world clinical practice.

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Section: Discussionmentioning

confidence: 99%

Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population

Zhang,

Dai,

Zhang

et al. 2023

Front. Endocrinol.

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Fibrinogen-to-Albumin Ratio and Glucose Metabolic States in Patients With Coronary Heart Disease

Wang,

Guo,

Huang

et al. 2023

Angiology

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This study investigated the relationship between fibrinogen-to-albumin ratio (FAR) and glucose metabolic state in patients with coronary heart disease (CHD). A total of 52,062 patients were enrolled in this study. Patients were classified according to FAR tertiles (T1: FAR < 0.0073; T2: 0.0073 ≤ FAR ≤ 0.0886; T3: FAR ≥ 0.0887). Patients were also classified into the normal glucose regulation (NGR) and elevated blood glucose (EBG) groups. The relationship between FAR and EBG was analyzed using logistic regression, and the association was evaluated according to sex and age. Among the participants, 32,471 (62.4%) had EBG, which was positively associated with FAR (odds ratio [OR], 1.19; 95% confidence interval [CI] 1.15-1.23). The OR of the FAR for EBG in males was higher than that in females (1.25; 95% CI 1.18-1.33 vs 1.15; 95% CI 1.10-1.20). Moreover, the OR of FAR for EBG was greater in patients aged 60 or younger (OR: 1.25; 95% CI 1.18-1.33) than in the elderly patients (over 60 years of age) (OR: 1.15; 95% CI 1.10-1.20). The results indicated a significant relationship between FAR and EBG and this association was higher in males and middle-aged patients.

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Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy

Cubillos

Monckeberg

Plaza

et al. 2023

BMC Pregnancy Childbirth

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Background Early prediction of Gestational Diabetes Mellitus (GDM) risk is of particular importance as it may enable more efficacious interventions and reduce cumulative injury to mother and fetus. The aim of this study is to develop machine learning (ML) models, for the early prediction of GDM using widely available variables, facilitating early intervention, and making possible to apply the prediction models in places where there is no access to more complex examinations. Methods The dataset used in this study includes registries from 1,611 pregnancies. Twelve different ML models and their hyperparameters were optimized to achieve early and high prediction performance of GDM. A data augmentation method was used in training to improve prediction results. Three methods were used to select the most relevant variables for GDM prediction. After training, the models ranked with the highest Area under the Receiver Operating Characteristic Curve (AUCROC), were assessed on the validation set. Models with the best results were assessed in the test set as a measure of generalization performance. Results Our method allows identifying many possible models for various levels of sensitivity and specificity. Four models achieved a high sensitivity of 0.82, a specificity in the range 0.72–0.74, accuracy between 0.73–0.75, and AUCROC of 0.81. These models required between 7 and 12 input variables. Another possible choice could be a model with sensitivity of 0.89 that requires just 5 variables reaching an accuracy of 0.65, a specificity of 0.62, and AUCROC of 0.82. Conclusions The principal findings of our study are: Early prediction of GDM within early stages of pregnancy using regular examinations/exams; the development and optimization of twelve different ML models and their hyperparameters to achieve the highest prediction performance; a novel data augmentation method is proposed to allow reaching excellent GDM prediction results with various models.

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Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study

Cited by 4 publications

References 46 publications

Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population

Integration of clinical demographics and routine laboratory analysis parameters for early prediction of gestational diabetes mellitus in the Chinese population

Fibrinogen-to-Albumin Ratio and Glucose Metabolic States in Patients With Coronary Heart Disease

Development of machine learning models to predict gestational diabetes risk in the first half of pregnancy

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