Materiały źródłowe

• #1 Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11356194/

  Gestational diabetes mellitus (GDM) may impact both maternal and fetal/neonatal health. The identification of prognostic indicators for GDM may improve risk assessment and selection of patient for intensive monitoring. The aim of this study was to find potential predictors of adverse pregnancy outcome in GDM and normoglycemic patients by comparing the levels of different biochemical parameters and the values of blood cell count (BCC) between GDM and normoglycemic patients and between patients with adverse and good outcome. […] The results of our study demonstrated that the best prognostic potential in GDM showed inflammation related parameters, identifying fibrinogen as a parameter with both diagnostic and prognostic ability. […] The evaluated pregnancy complications were more often seen in GDM. The most frequent pregnancy/delivery complication in the GDM group was preterm delivery, which may be explained by pathophysiology of GDM and preterm delivery which implies the involvement of (glycol)oxidative stress and inflammation.

• #2 Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

  https://www.mdpi.com/1648-9144/60/8/1250

  Gestational diabetes mellitus (GDM) may impact both maternal and fetal/neonatal health. The identification of prognostic indicators for GDM may improve risk assessment and selection of patient for intensive monitoring. The aim of this study was to find potential predictors of adverse pregnancy outcome in GDM and normoglycemic patients by comparing the levels of different biochemical parameters and the values of blood cell count (BCC) between GDM and normoglycemic patients and between patients with adverse and good outcome. […] The results of our study demonstrated that the best prognostic potential in GDM showed inflammation related parameters, identifying fibrinogen as a parameter with both diagnostic and prognostic ability. […] Concerning the fact that prevalence of GDM is dramatically increasing worldwide with short- and long-term consequences for maternal and fetal/neonatal/infant health, there is a need for the identification of biomarkers that can be used for the prediction of adverse pregnancy outcome in GDM patients and indicate intensive monitoring of those pregnancies. The results of our study identified fibrinogen as a parameter with both diagnostic and prognostic ability in GDM. Furthermore, inflammation-related parameters demonstrated prognostic potential in our study group. The results are promising in terms of the identification of potential biomarkers useful for the prediction of adverse pregnancy outcome, and future multicenter studies are needed in order to confirm and expand our results.

• #3 Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

  https://www.mdpi.com/1648-9144/60/8/1250

  Gestational diabetes mellitus (GDM) may impact both maternal and fetal/neonatal health. The identification of prognostic indicators for GDM may improve risk assessment and selection of patient for intensive monitoring. The aim of this study was to find potential predictors of adverse pregnancy outcome in GDM and normoglycemic patients by comparing the levels of different biochemical parameters and the values of blood cell count (BCC) between GDM and normoglycemic patients and between patients with adverse and good outcome. […] The results of our study demonstrated that the best prognostic potential in GDM showed inflammation related parameters, identifying fibrinogen as a parameter with both diagnostic and prognostic ability. […] Concerning the fact that prevalence of GDM is dramatically increasing worldwide with short- and long-term consequences for maternal and fetal/neonatal/infant health, there is a need for the identification of biomarkers that can be used for the prediction of adverse pregnancy outcome in GDM patients and indicate intensive monitoring of those pregnancies. The results of our study identified fibrinogen as a parameter with both diagnostic and prognostic ability in GDM. Furthermore, inflammation-related parameters demonstrated prognostic potential in our study group. The results are promising in terms of the identification of potential biomarkers useful for the prediction of adverse pregnancy outcome, and future multicenter studies are needed in order to confirm and expand our results.

• #4 Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11356194/

  Gestational diabetes mellitus (GDM) may impact both maternal and fetal/neonatal health. The identification of prognostic indicators for GDM may improve risk assessment and selection of patient for intensive monitoring. The aim of this study was to find potential predictors of adverse pregnancy outcome in GDM and normoglycemic patients by comparing the levels of different biochemical parameters and the values of blood cell count (BCC) between GDM and normoglycemic patients and between patients with adverse and good outcome. […] The results of our study demonstrated that the best prognostic potential in GDM showed inflammation related parameters, identifying fibrinogen as a parameter with both diagnostic and prognostic ability. […] The evaluated pregnancy complications were more often seen in GDM. The most frequent pregnancy/delivery complication in the GDM group was preterm delivery, which may be explained by pathophysiology of GDM and preterm delivery which implies the involvement of (glycol)oxidative stress and inflammation.

• #5 Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11356194/

  Increased levels of fibrinogen were determined in GDM patients compared to normoglycemic controls, as well as in GDM patients with poor pregnancy outcome. […] The results are promising in terms of the identification of potential biomarkers useful for the prediction of adverse pregnancy outcome, and future multicenter studies are needed in order to confirm and expand our results.

• #6 Advancement in predictive biomarkers for gestational diabetes mellitus diagnosis and related outcomes: a scoping review – PubMed

  https://pubmed.ncbi.nlm.nih.gov/39675825

  Gestational diabetes mellitus (GDM) is a metabolic disorder associated with adverse maternal and neonatal outcomes. […] A total of 278 biomarkers with significant changes in individuals with GDM compared with controls were identified. The univariate predictive biomarkers exhibited insufficient clinical sensitivity and specificity for predicting GDM, perinatal outcomes, and the necessity of medication. Multivariable models combining maternal risk factors with biomarkers provided more accurate detection but required validation for use in clinical settings. […] This review recommends further research integrating novel omics technology for building accurate models for predicting GDM, perinatal outcome, and the necessity of medication while considering the optimal testing time.

• #7 A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women | BMC Pregnancy and Childbirth | Full Text

  https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-019-2374-8

  Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. The aim of the present study was to build a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801). This GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary. The prevalence of GDM in Chinese women ranged between 13.0 and 20.9%, and the variation was partly due to different criteria. It is important to predict the risk of GDM early in pregnancy to enable early interventions to prevent GDM. The incidence of GDM in this study (12.8%) was lower than the overall frequency (17.8%) reported in HAPO study. Women with GDM also had elevated FPG and TG levels in the early stage of pregnancy, and as a consequence had an increased risk of adverse perinatal outcomes such as high birth weight (e.g., macrosomia or LGA) and caesarean section. The model developed in this study using information on maternal factors such as age, prepregnancy BMI, FPG and TG and using robust modeling methods is the first model applicable to Chinese women for whom no ethnicity-specific GDM risk prediction model has been available. The strengths of this study include a moderate sample size, a robust modeling strategy for correlated predictors, and its development of a simple formula for prediction based on only four maternal factors (age, prepregnancy BMI, FPG and TG). This formula can be applied even if only a calculator is available, such as in less-developed parts of China. From 8th to 20th gestational week, the average FPG level indicated a slight decreasing trend while, in line with previous studies, TG levels displayed a slight increasing trend. The metabolites were intercorrelated during this period of pregnancy. We developed a prediction model in Chinese women, which addressed the correlation of predictors and incorporated maternal age, prepregnancy BMI, FPG and TG, with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801). Thus, a simple model, which can be applied using a hand calculator, may predict the risk of GDM and be used in less economically developed parts of China.

• #8 A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women | BMC Pregnancy and Childbirth | Full Text

  https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-019-2374-8

  Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. The aim of the present study was to build a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801). This GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary. The prevalence of GDM in Chinese women ranged between 13.0 and 20.9%, and the variation was partly due to different criteria. It is important to predict the risk of GDM early in pregnancy to enable early interventions to prevent GDM. The incidence of GDM in this study (12.8%) was lower than the overall frequency (17.8%) reported in HAPO study. Women with GDM also had elevated FPG and TG levels in the early stage of pregnancy, and as a consequence had an increased risk of adverse perinatal outcomes such as high birth weight (e.g., macrosomia or LGA) and caesarean section. The model developed in this study using information on maternal factors such as age, prepregnancy BMI, FPG and TG and using robust modeling methods is the first model applicable to Chinese women for whom no ethnicity-specific GDM risk prediction model has been available. The strengths of this study include a moderate sample size, a robust modeling strategy for correlated predictors, and its development of a simple formula for prediction based on only four maternal factors (age, prepregnancy BMI, FPG and TG). This formula can be applied even if only a calculator is available, such as in less-developed parts of China. From 8th to 20th gestational week, the average FPG level indicated a slight decreasing trend while, in line with previous studies, TG levels displayed a slight increasing trend. The metabolites were intercorrelated during this period of pregnancy. We developed a prediction model in Chinese women, which addressed the correlation of predictors and incorporated maternal age, prepregnancy BMI, FPG and TG, with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801). Thus, a simple model, which can be applied using a hand calculator, may predict the risk of GDM and be used in less economically developed parts of China.

• #9 Electronic Health Record Driven Prediction for Gestational Diabetes Mellitus in Early Pregnancy | Scientific Reports

  https://www.nature.com/articles/s41598-017-16665-y

  Secondary analysis of EHRs promises to advance clinical research and better inform clinical decision making, but challenges in temporal representation and systems discrimination ability of EHRs prevent widespread practice of predictive modelling. […] To improve the performance of the prediction model on imbalanced data, cost-sensitive learning was taken as a potential method. […] We therefore conducted this research with EHRs to identify the most feasible algorithm for predicting GDM, potentially advancing the diagnosis period of GDM and prognosis of its outcomes. […] The model for early diagnosis of GDM by machine learning (ML) had the ability to predict the unknown GDM status of pregnant women in their early pregnancy by exploring the value of EHRs, including archival data, examination data and diagnostic data of OGTT.

• #10 Electronic Health Record Driven Prediction for Gestational Diabetes Mellitus in Early Pregnancy | Scientific Reports

  https://www.nature.com/articles/s41598-017-16665-y

  The prediction accuracy of negative samples is high (99.8%). Among those predicted positive instances, the results suggest that the vast majority (98.4%) are real GDM class according to OGTT. […] Our results also suggest that although CSHM takes lower confidence in prediction than a simple classifier, it is very good at prediction of GDM with higher AUC than those of the others. […] The possibility of first-trimester identification of women at greatest risk of GDM, with subsequent implementation of possible lifestyle or medical interventions at this stage, requires further study.

• #11 Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan Environment and Children’s Study | Scientific Reports

  https://www.nature.com/articles/s41598-023-44313-1

  Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. […] The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data. […] GBDT exhibited the highest accuracy, followed by LR, RF, and SVM. […] The accuracy for GDM prediction of all algorithms, except for SVM, improved without overfitting using undersampling or oversampling. […] The TPRs of the RF, GBDT, and LR models were improved by changing the sampling method. […] In this study, we predicted the development of GDM based on information that could be collected in the early stages of pregnancy. […] If a high-risk group near the 1st trimester can be extracted, it may lead to GDM prevention. […] In conclusion, we demonstrated that exploratory analysis using AI for a large birth cohort is possible through the appropriate use of algorithms.

• #12 Prediction of gestational diabetes mellitus using machine learning from birth cohort data of the Japan Environment and Children’s Study | Scientific Reports

  https://www.nature.com/articles/s41598-023-44313-1

  Recently, prediction of gestational diabetes mellitus (GDM) using artificial intelligence (AI) from medical records has been reported. […] The results of decision tree-based algorithms, such as GBDT, have shown high accuracy, interpretability, and superiority for predicting GDM using birth cohort data. […] GBDT exhibited the highest accuracy, followed by LR, RF, and SVM. […] The accuracy for GDM prediction of all algorithms, except for SVM, improved without overfitting using undersampling or oversampling. […] The TPRs of the RF, GBDT, and LR models were improved by changing the sampling method. […] In this study, we predicted the development of GDM based on information that could be collected in the early stages of pregnancy. […] If a high-risk group near the 1st trimester can be extracted, it may lead to GDM prevention. […] In conclusion, we demonstrated that exploratory analysis using AI for a large birth cohort is possible through the appropriate use of algorithms.

• #13 The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

  https://www.mdpi.com/1660-4601/17/9/3048

  Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM.

• #14 Gestational diabetes – Wikipedia

  https://en.wikipedia.org/wiki/Gestational_diabetes

  Gestational diabetes generally resolves once the baby is born. […] Women diagnosed with gestational diabetes have an increased risk of developing diabetes mellitus in the future. The risk is highest in women who needed insulin treatment, had antibodies associated with diabetes, women with more than two previous pregnancies, and women who were obese. […] Women requiring insulin to manage gestational diabetes have a 50% risk of developing diabetes within the next five years. […] Depending on the population studied, the diagnostic criteria and the length of follow-up, the risk can vary enormously. […] Children of women with GDM have an increased risk for childhood and adult obesity and an increased risk of glucose intolerance and type 2 diabetes later in life. […] The relative benefits and harms of different oral anti-diabetic medications are not yet well understood as of 2017. […] Research is being conducted to develop a web-based clinical decision support system for GDM prediction using machine learning techniques. Results so far demonstrated great potential in clinical practicality for automatic GDM prognosis.

• #15 Gestational diabetes – Wikipedia

  https://en.wikipedia.org/wiki/Gestational_diabetes

  Gestational diabetes generally resolves once the baby is born. […] Women diagnosed with gestational diabetes have an increased risk of developing diabetes mellitus in the future. The risk is highest in women who needed insulin treatment, had antibodies associated with diabetes, women with more than two previous pregnancies, and women who were obese. […] Women requiring insulin to manage gestational diabetes have a 50% risk of developing diabetes within the next five years. […] Depending on the population studied, the diagnostic criteria and the length of follow-up, the risk can vary enormously. […] Children of women with GDM have an increased risk for childhood and adult obesity and an increased risk of glucose intolerance and type 2 diabetes later in life. […] The relative benefits and harms of different oral anti-diabetic medications are not yet well understood as of 2017. […] Research is being conducted to develop a web-based clinical decision support system for GDM prediction using machine learning techniques. Results so far demonstrated great potential in clinical practicality for automatic GDM prognosis.

• #16 Gestational diabetes – Wikipedia

  https://en.wikipedia.org/wiki/Gestational_diabetes

  Gestational diabetes generally resolves once the baby is born. […] Women diagnosed with gestational diabetes have an increased risk of developing diabetes mellitus in the future. The risk is highest in women who needed insulin treatment, had antibodies associated with diabetes, women with more than two previous pregnancies, and women who were obese. […] Women requiring insulin to manage gestational diabetes have a 50% risk of developing diabetes within the next five years. […] Depending on the population studied, the diagnostic criteria and the length of follow-up, the risk can vary enormously. […] Children of women with GDM have an increased risk for childhood and adult obesity and an increased risk of glucose intolerance and type 2 diabetes later in life. […] The relative benefits and harms of different oral anti-diabetic medications are not yet well understood as of 2017. […] Research is being conducted to develop a web-based clinical decision support system for GDM prediction using machine learning techniques. Results so far demonstrated great potential in clinical practicality for automatic GDM prognosis.

• #17

  https://link.springer.com/article/10.1007/s11892-023-01516-0

  Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. […] The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM. […] Women with a history of GDM have a greater than sevenfold risk of developing postpartum glucose intolerance than those who were normoglycemic.

• #18 Gestational diabetes – Wikipedia

  https://en.wikipedia.org/wiki/Gestational_diabetes

  Gestational diabetes generally resolves once the baby is born. […] Women diagnosed with gestational diabetes have an increased risk of developing diabetes mellitus in the future. The risk is highest in women who needed insulin treatment, had antibodies associated with diabetes, women with more than two previous pregnancies, and women who were obese. […] Women requiring insulin to manage gestational diabetes have a 50% risk of developing diabetes within the next five years. […] Depending on the population studied, the diagnostic criteria and the length of follow-up, the risk can vary enormously. […] Children of women with GDM have an increased risk for childhood and adult obesity and an increased risk of glucose intolerance and type 2 diabetes later in life. […] The relative benefits and harms of different oral anti-diabetic medications are not yet well understood as of 2017. […] Research is being conducted to develop a web-based clinical decision support system for GDM prediction using machine learning techniques. Results so far demonstrated great potential in clinical practicality for automatic GDM prognosis.

• #19 Gestational diabetes – Wikipedia

  https://en.wikipedia.org/wiki/Gestational_diabetes

  Gestational diabetes generally resolves once the baby is born. […] Women diagnosed with gestational diabetes have an increased risk of developing diabetes mellitus in the future. The risk is highest in women who needed insulin treatment, had antibodies associated with diabetes, women with more than two previous pregnancies, and women who were obese. […] Women requiring insulin to manage gestational diabetes have a 50% risk of developing diabetes within the next five years. […] Depending on the population studied, the diagnostic criteria and the length of follow-up, the risk can vary enormously. […] Children of women with GDM have an increased risk for childhood and adult obesity and an increased risk of glucose intolerance and type 2 diabetes later in life. […] The relative benefits and harms of different oral anti-diabetic medications are not yet well understood as of 2017. […] Research is being conducted to develop a web-based clinical decision support system for GDM prediction using machine learning techniques. Results so far demonstrated great potential in clinical practicality for automatic GDM prognosis.

• #20 Electronic Health Record Driven Prediction for Gestational Diabetes Mellitus in Early Pregnancy | Scientific Reports

  https://www.nature.com/articles/s41598-017-16665-y

  Gestational diabetes mellitus (GDM) is conventionally confirmed with oral glucose tolerance test (OGTT) in 24 to 28 weeks of gestation, but it is still uncertain whether it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy. […] To our knowledge, this is the first study to apply machine learning models with EHRs to predict GDM, which will facilitate personalized medicine in maternal health management in the future. […] GDM increases the risk of development of type 2 diabetes mellitus in both mother and child, is also associated with adverse short-term fetal outcomes and offspring long-term greater adiposity. […] A technique with a high sensitivity to predicate GDM at the first-trimester would be well-received for the clinical practitioners and almost all pregnant women, decreasing the future risks of development of GDM.

• #21 Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

  https://pmc.ncbi.nlm.nih.gov/articles/PMC11356194/

  Gestational diabetes mellitus (GDM) may impact both maternal and fetal/neonatal health. The identification of prognostic indicators for GDM may improve risk assessment and selection of patient for intensive monitoring. The aim of this study was to find potential predictors of adverse pregnancy outcome in GDM and normoglycemic patients by comparing the levels of different biochemical parameters and the values of blood cell count (BCC) between GDM and normoglycemic patients and between patients with adverse and good outcome. […] The results of our study demonstrated that the best prognostic potential in GDM showed inflammation related parameters, identifying fibrinogen as a parameter with both diagnostic and prognostic ability. […] The evaluated pregnancy complications were more often seen in GDM. The most frequent pregnancy/delivery complication in the GDM group was preterm delivery, which may be explained by pathophysiology of GDM and preterm delivery which implies the involvement of (glycol)oxidative stress and inflammation.

• #22 Screening and Diagnosis of Gestational Diabetes Mellitus | Effective Health Care (EHC) Program

  https://effectivehealthcare.ahrq.gov/products/gestational-diabetes-screening-diagnosis/research-protocol

  Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance that either has its onset or first becomes apparent during pregnancy. Disappearance of GDM postpartum is critical, as previously undiagnosed type 2 diabetes can be mistaken for GDM. Current prevalence estimates for GDM range from approximately 1 to 14 percent of pregnancies in the United States, depending on population characteristics, such as ethnicity and clinical status. GDM incidence has increased over the past decades, alongside the increase in rates of obesity and type 2 diabetes, and these trends are expected to continue to rise. […] […] GDM is an important public health concern, as impaired glucose tolerance may affect maternal and fetal health outcomes. Mothers may face an increased risk of labor and birth complications, psychological issues, and an increased likelihood of developing diabetes later in life. Risks for the fetus include macrosomia (excessive birth weight) and birth injuries, such as shoulder dystocia, nerve palsies, and fractures. In addition, risk of glucose intolerance and obesity in childhood is associated with GDM. […]

• #23 The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

  https://www.mdpi.com/1660-4601/17/9/3048

  A personalised approach would stratify women with GDM by the estimated risk of pregnancy complications. Those at high risk would maximally benefit from the targeted delivery of evidence-based preventative and therapeutic interventions. Those at low risk would be spared unnecessary treatment and may be offered less intensive intervention. Accurate risk prediction models working within existing diagnostic definitions and utilising predictors readily available in routine care, could be implementable in clinical care and would be feasible and scalable. From a public health perspective, this could enable a risk-stratified approach and development of new models of care to better allocate scarce healthcare resources, imperative in the context of the increasing GDM prevalence. […] This systematic review identified five prediction modelling studies for pregnancy complications in women with GDM. Approaches to prediction varied, but the birth of an LGA neonate was the leading outcome, whether as part of a composite or singularly. Models seeking to predict a single outcome were more discriminatory than those predicting a composite outcome. Three predictors emerged in most models: glycaemic measures, BMI, and maternal age. Predictive performance was generally inadequately reported, and external validation was lacking. All models had a high risk of bias due to methodologic limitations in analysis as assessed by PROBAST.

• #24

  https://link.springer.com/article/10.1007/s11892-023-01516-0

  Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. […] The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM. […] Women with a history of GDM have a greater than sevenfold risk of developing postpartum glucose intolerance than those who were normoglycemic.

• #25

  https://link.springer.com/article/10.1007/s11892-023-01516-0

  Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. […] The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM. […] Women with a history of GDM have a greater than sevenfold risk of developing postpartum glucose intolerance than those who were normoglycemic.

• #26

  https://link.springer.com/article/10.1007/s11892-023-01516-0

  The predictive performance of 13 studies that reported the area under the curve ranged from 0.66 to 0.92. However, none were externally validated. Only a few models were validated internally. […] This systematic review of risk models predicting postpartum glucose intolerance among women who had GDM identified 15 models; however, none were externally validated and less than half were internally validated. […] Identification of those at risk can facilitate targeted screening and prevention strategies. Despite this, our systematic review identified that existing prognostic models for glucose intolerance following GDM were not externally validated, and only a few were internally validated.

• #27 The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

  https://www.mdpi.com/1660-4601/17/9/3048

  A personalised approach would stratify women with GDM by the estimated risk of pregnancy complications. Those at high risk would maximally benefit from the targeted delivery of evidence-based preventative and therapeutic interventions. Those at low risk would be spared unnecessary treatment and may be offered less intensive intervention. Accurate risk prediction models working within existing diagnostic definitions and utilising predictors readily available in routine care, could be implementable in clinical care and would be feasible and scalable. From a public health perspective, this could enable a risk-stratified approach and development of new models of care to better allocate scarce healthcare resources, imperative in the context of the increasing GDM prevalence. […] This systematic review identified five prediction modelling studies for pregnancy complications in women with GDM. Approaches to prediction varied, but the birth of an LGA neonate was the leading outcome, whether as part of a composite or singularly. Models seeking to predict a single outcome were more discriminatory than those predicting a composite outcome. Three predictors emerged in most models: glycaemic measures, BMI, and maternal age. Predictive performance was generally inadequately reported, and external validation was lacking. All models had a high risk of bias due to methodologic limitations in analysis as assessed by PROBAST.

• #28

  https://link.springer.com/article/10.1007/s11892-023-01516-0

  Despite the crucial role that prediction models play in guiding early risk stratification and timely intervention to prevent type 2 diabetes after gestational diabetes mellitus (GDM), their use is not widespread in clinical practice. […] The existing prognostic models for glucose intolerance following GDM have various methodological shortcomings, with only a few models being assessed to have low risk of bias and validated internally. Future research should prioritize the development of robust, high-quality risk prediction models that follow appropriate guidelines, in order to advance this area and improve early risk stratification and intervention for glucose intolerance and type 2 diabetes among women who have had GDM. […] Women with a history of GDM have a greater than sevenfold risk of developing postpartum glucose intolerance than those who were normoglycemic.

• #29 Electronic Health Record Driven Prediction for Gestational Diabetes Mellitus in Early Pregnancy | Scientific Reports

  https://www.nature.com/articles/s41598-017-16665-y

  The prediction accuracy of negative samples is high (99.8%). Among those predicted positive instances, the results suggest that the vast majority (98.4%) are real GDM class according to OGTT. […] Our results also suggest that although CSHM takes lower confidence in prediction than a simple classifier, it is very good at prediction of GDM with higher AUC than those of the others. […] The possibility of first-trimester identification of women at greatest risk of GDM, with subsequent implementation of possible lifestyle or medical interventions at this stage, requires further study.

• #30 Gestational diabetes – Wikipedia

  https://en.wikipedia.org/wiki/Gestational_diabetes

  Gestational diabetes generally resolves once the baby is born. […] Women diagnosed with gestational diabetes have an increased risk of developing diabetes mellitus in the future. The risk is highest in women who needed insulin treatment, had antibodies associated with diabetes, women with more than two previous pregnancies, and women who were obese. […] Women requiring insulin to manage gestational diabetes have a 50% risk of developing diabetes within the next five years. […] Depending on the population studied, the diagnostic criteria and the length of follow-up, the risk can vary enormously. […] Children of women with GDM have an increased risk for childhood and adult obesity and an increased risk of glucose intolerance and type 2 diabetes later in life. […] The relative benefits and harms of different oral anti-diabetic medications are not yet well understood as of 2017. […] Research is being conducted to develop a web-based clinical decision support system for GDM prediction using machine learning techniques. Results so far demonstrated great potential in clinical practicality for automatic GDM prognosis.

• #31 Gestational Diabetes: Causes, Symptoms & Treatment

  https://my.clevelandclinic.org/health/diseases/9012-gestational-diabetes

  Gestational diabetes is a common condition and healthcare providers have a good idea of how best to manage and treat it. You’ll still have a healthy pregnancy and a healthy baby if you have gestational diabetes. Work with your healthcare provider to make sure you understand your treatment plan and how you can keep your blood sugar levels healthy. […] Take time to understand the possible complications of not managing gestational diabetes. Your baby has a very good chance of being born healthy, but you must take steps to manage the condition. If your blood sugar levels are high several readings in a row, don’t wait to contact your provider. Let them know that your blood sugar levels are repeatedly high so they can adjust your foods or medication and help you. Gestational diabetes is manageable, but there’s a level of responsibility you must take to ensure your pregnancy is healthy.

• #32 Gestational Diabetes: Causes, Symptoms & Treatment

  https://my.clevelandclinic.org/health/diseases/9012-gestational-diabetes

  Yes, having gestational diabetes may make your pregnancy high risk. Healthcare providers consider a pregnancy high risk when either you or the fetus (or both) has health conditions that increase your chances of having a pregnancy complication. […] Yes. Most babies born are born healthy. There are some steps you can take to manage gestational diabetes during pregnancy to give your child the best start in life. Attending all your prenatal appointments and managing diabetes the best you can during pregnancy are the two best things you can do.

• #33 Gestational Diabetes: Causes, Symptoms & Treatment

  https://my.clevelandclinic.org/health/diseases/9012-gestational-diabetes

  Yes, having gestational diabetes may make your pregnancy high risk. Healthcare providers consider a pregnancy high risk when either you or the fetus (or both) has health conditions that increase your chances of having a pregnancy complication. […] Yes. Most babies born are born healthy. There are some steps you can take to manage gestational diabetes during pregnancy to give your child the best start in life. Attending all your prenatal appointments and managing diabetes the best you can during pregnancy are the two best things you can do.

• #34 Screening and Diagnosis of Gestational Diabetes Mellitus | Effective Health Care (EHC) Program

  https://effectivehealthcare.ahrq.gov/products/gestational-diabetes-screening-diagnosis/research-protocol

  Treatment aims to minimize the risk of adverse maternal and child outcomes associated with glucose intolerance in women diagnosed with GDM. First-line treatment for GDM involves diet modification, glucose monitoring, and moderate exercise. When dietary management fails to achieve glucose control, insulin or oral antidiabetic medications may be used. Because clinical uncertainty exists regarding whether treatment to reduce maternal glucose levels decreases the risks associated with GDM, professional associations disagree on screening recommendations. […] […] The primary aims of this review are to: a) identify the test properties of screening and diagnostic tests for GDM; b) evaluate the potential benefits and harms of screening at 24 weeks and 24 weeks gestation; and c) determine the effects of treatment in modifying outcomes for women diagnosed with GDM. The benefits and harms of treatments will be considered in this review in order to determine the downstream effects of screening on health outcomes. […]

• #35 A simple model to predict risk of gestational diabetes mellitus from 8 to 20 weeks of gestation in Chinese women | BMC Pregnancy and Childbirth | Full Text

  https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-019-2374-8

  Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Screening for GDM and applying adequate interventions may reduce the risk of adverse outcomes. The aim of the present study was to build a simple model to predict GDM in early pregnancy in Chinese women using biochemical markers and machine learning algorithm. The risk of GDM could be predicted with maternal age, prepregnancy body mass index (BMI), FPG and TG with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801). This GDM prediction model is simple and potentially applicable in Chinese women. Further validation is necessary. The prevalence of GDM in Chinese women ranged between 13.0 and 20.9%, and the variation was partly due to different criteria. It is important to predict the risk of GDM early in pregnancy to enable early interventions to prevent GDM. The incidence of GDM in this study (12.8%) was lower than the overall frequency (17.8%) reported in HAPO study. Women with GDM also had elevated FPG and TG levels in the early stage of pregnancy, and as a consequence had an increased risk of adverse perinatal outcomes such as high birth weight (e.g., macrosomia or LGA) and caesarean section. The model developed in this study using information on maternal factors such as age, prepregnancy BMI, FPG and TG and using robust modeling methods is the first model applicable to Chinese women for whom no ethnicity-specific GDM risk prediction model has been available. The strengths of this study include a moderate sample size, a robust modeling strategy for correlated predictors, and its development of a simple formula for prediction based on only four maternal factors (age, prepregnancy BMI, FPG and TG). This formula can be applied even if only a calculator is available, such as in less-developed parts of China. From 8th to 20th gestational week, the average FPG level indicated a slight decreasing trend while, in line with previous studies, TG levels displayed a slight increasing trend. The metabolites were intercorrelated during this period of pregnancy. We developed a prediction model in Chinese women, which addressed the correlation of predictors and incorporated maternal age, prepregnancy BMI, FPG and TG, with a predictive accuracy of 0.64 and an AUC of 0.766 (95% CI 0.731, 0.801). Thus, a simple model, which can be applied using a hand calculator, may predict the risk of GDM and be used in less economically developed parts of China.

• #36 Gestational diabetes – Wikipedia

  https://en.wikipedia.org/wiki/Gestational_diabetes

  Gestational diabetes generally resolves once the baby is born. […] Women diagnosed with gestational diabetes have an increased risk of developing diabetes mellitus in the future. The risk is highest in women who needed insulin treatment, had antibodies associated with diabetes, women with more than two previous pregnancies, and women who were obese. […] Women requiring insulin to manage gestational diabetes have a 50% risk of developing diabetes within the next five years. […] Depending on the population studied, the diagnostic criteria and the length of follow-up, the risk can vary enormously. […] Children of women with GDM have an increased risk for childhood and adult obesity and an increased risk of glucose intolerance and type 2 diabetes later in life. […] The relative benefits and harms of different oral anti-diabetic medications are not yet well understood as of 2017. […] Research is being conducted to develop a web-based clinical decision support system for GDM prediction using machine learning techniques. Results so far demonstrated great potential in clinical practicality for automatic GDM prognosis.

• #37 The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

  https://www.mdpi.com/1660-4601/17/9/3048

  This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, limitations in current models have been identified and this emphasises that future model development and validation would benefit from the application of methodologic advances in this rapidly evolving field. External validation, including appropriate reporting of calibration and formal evaluation of clinical usefulness with decision curve-analysis, will significantly assist the translation of promising statistical models into a useful clinical tool. Such a tool would be capable of improving outcomes for women with GDM by enhancing clinical decision-making and facilitating the stratification of affected women by their risk of pregnancy complications, thus enabling a personalised model-of-care.

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