A mid‑pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings

Zhang, Ya‐Zhong; Zhou, Lei; Tian, Li; Li, Xin; Zhang, Guyue; Qin, Jiang‐Yuan; Zhang, Dandan; Fang, Hui

doi:10.3892/etm.2020.8690

Cited by 13 publications

(9 citation statements)

References 41 publications

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“…In this study, 19 prediction models were established using the LR models [ 17 , 18 , 20 - 23 , 26 - 33 , 35 - 39 ], and the overall pooled AUROC for the LR models for predicting GDM was 0.8151 ( Figure 5 ). The overall pooled AUROC for non-LR models to predict GDM was 0.8891 ( Figure 6 ), the highest value among these subgroups.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis

Zhang¹,

Yang²,

Han³

et al. 2022

J Med Internet Res

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Background Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. Objective The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. Methods Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. Results A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. Conclusions Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis

Zhang¹,

Yang²,

Han³

et al. 2022

J Med Internet Res

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“…This strategy was employed to ensure the model could be used in situations where laboratory measurements were not readily available. We selected nine variables for GDM prediction after reviewing 28 published studies that reported findings from cohort and case–control studies, diagnosis or assessments of GDM, or development of predictive or risk models 27 – 30 , 58 – 81 (listed and summarized in Table S1 and Figure S1 ) and in accordance with the information available for the CME study. The selected variables included maternal age (years); family history of type 2 diabetes (yes/no); previous diagnosis of hypertension (yes/no); BMI (kg/m 2 ); gestational week at first prenatal visit; parity (number); birth weight of last child (grams); type of capillary glucose measurement at first prenatal visit (random/fasting); and capillary blood glucose level at first prenatal visit (mg/dL).…”

Section: Methodsmentioning

confidence: 99%

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women

Gallardo-Rincón

Ríos-Blancas

Ortega-Montiel³

et al. 2023

Sci Rep

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Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study ‘Cuido mi embarazo’. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision.

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“…In recent years, GD predictive models have been established to identify pregnant women at high risk and to provide a scientific basis for the early detection of high‐risk GD pregnancies 29,30 . However, obtaining clinical biochemical indicators or the imaging data of pregnant women for use in prediction models is challenging, thus reducing each model's applicability.…”

Section: Introductionmentioning

confidence: 99%

Establishment and validation of a prediction model for gestational diabetes

Wang,

He,

et al. 2023

Diabetes Obesity Metabolism

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AimTo develop a visual prediction model for gestational diabetes (GD) in pregnant women and to establish an effective and practical tool for clinical application.MethodsTo establish a prediction model, the modelling set included 1756 women enrolled in the Zunyi birth cohort, the internal validation set included 1234 enrolled women, and pregnant women in the Wuhan cohort were included in the external validation set. We established a demographic–lifestyle factor model (DLFM) and a demographic–lifestyle–environmental pollution factor model (DLEFM) based on whether the women were exposed to environmental pollutants. The least absolute shrinkage and selection lasso–logistic regression analyses were used to identify the independent predictors of GD and construct a nomogram for predicting its occurrence.ResultsThe DLEFM regression analysis showed that a family history of diabetes (odd ratio [OR] 2.28; 95% confidence interval [CI] 1.05‐4.71), a history of GD in pregnant women (OR 4.22; 95% CI 1.89‐9.41), being overweight or obese before pregnancy (OR 1.71; 95% CI 1.27‐2.29), a history of hypertension (OR 2.61; 95% CI 1.41‐4.72), sedentary time (h/day) (OR 1.16; 95% CI 1.08‐1.24), monobenzyl phthalate (OR 1.95; 95% CI 1.45‐2.67) and Q4 mono‐ethyl phthalate concentration (OR 1.85; 95% CI 1.26‐2.73) were independent predictors. The area under the receiver operating curves for the internal validation of the DLEFM and the DLFM constructed using these seven factors was 0.827 and 0.783, respectively. The calibration curve of the DLEFM was close to the diagonal line. The DLEFM was thus the more optimal model, and the one which we chose.ConclusionsA nomogram based on preconception factors was constructed to predict the occurrence of GD in the second and third trimesters. It provided an effective tool for the early prediction and timely management of GD.

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A mid‑pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings

Cited by 13 publications

References 41 publications

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis

Machine Learning Prediction Models for Gestational Diabetes Mellitus: Meta-analysis

MIDO GDM: an innovative artificial intelligence-based prediction model for the development of gestational diabetes in Mexican women

Establishment and validation of a prediction model for gestational diabetes

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