Prediction model comparison for gestational diabetes mellitus with macrosomia based on risk factor investigation

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review

BACKGROUND

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies.

OBJECTIVES

To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice.

SEARCH STRATEGY

MEDLINE, EMBASE and Cochrane Library were searched until June 2022.

SELECTION CRITERIA

We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance.

DATA COLLECTION AND ANALYSIS

Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool).

MAIN RESULTS

A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias.

CONCLUSIONS

There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women

BACKGROUND

Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction.

METHODS

In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software.

RESULTS

We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model.

CONCLUSIONS

Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.

Predictive Value of Immune Cells in the Risk of Gestational Diabetes Mellitus: A Pilot Study

Aims

Immunological and biochemical parameters are gaining more and more importance in the prognosis of diabetes and its complications. Here, we assessed the predictive power of immune cells correlated with biochemical parameters in gestational diabetes mellitus (GDM).

Materials and Methods

Immune cells and serum biochemical parameters were determined in women with GDM and pregnant controls. Receiver operating characteristics (ROC) curve analyses were conducted to assess the optimal cutoff and value of ratios of immune cells to biochemical parameters for predicting GDM.

Results

Blood glucose, total cholesterol, LDL-cholesterol and triglycerides were significantly increased whereas HDL-cholesterol decreased in women with GDM compared to pregnant controls. Glycated hemoglobin, creatinine, transaminase activities did not significantly differ between both groups. Total leukocyte, lymphocyte and platelet numbers were significantly high in women with GDM. Correlation tests showed that ratios of lymphocyte/HDL-C, monocyte/HDL-C and granulocyte/HDL-C were significantly higher in women with GDM than in pregnant controls (p = 0.001; p = 0.009 and p = 0.004 respectively). Women with a lymphocyte/HDL-C ratio greater than 3.66 had a 4-fold increased risk of developing GDM than those with lower ratios (odds ratio 4.00; 95% CI: 1.094 – 14.630; p=0.041).

Conclusion

Our study showed that ratios of lymphocyte, monocyte and granulocyte to HDL-C might represent valuable biomarkers for GDM and in particular, lymphocyte/HDL-C ratio exhibited a strong predictive power for GDM risk.

Second-trimester maternal lipid profiles rather than glucose levels predict the occurrence of neonatal macrosomia regardless of glucose tolerance status: A matched cohort study in Beijing

AIMS

The mechanism underlying fetal overgrowth during pregnancy remains elusive. We aimed to establish a predictive model to identify the high-risk individuals with macrosomia in the second trimester of pregnancy.

DESIGN

A total of 2577 pregnant women with a routine 75-g oral glucose tolerance test during 24-28 gestational weeks were screened in a prospective cohort. Gestational diabetes mellitus (GDM) cases were 1:1 matching with age (±2 years) in normal glucose tolerance (NGT) ones from the same region. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were performed to determine the index and its inflection point for predicting macrosomia occurrence.

RESULTS

The data of perinatal outcomes of 565 GDM and 549 NGT who had given birth to single live babies at term were analyzed. Notably, we found serum apolipoprotein B (ApoB) level higher than 4.04 g/L combined with triglycerides (TG)/high-density lipoprotein cholesterol (HDLC) ratio above 1.36 formed the predictive model in both groups. The area under the ROC curve of this predictive model included ApoB and TG/HDL-C reached 0.807 (95% CI: 0.771-0.873) with a sensitivity of 71.9% and a specificity of 78.6%. Mediation analysis revealed that ApoB and TG/HDL-C ratio mediated the harmful effect of FBG on the risk of macrosomia.

CONCLUSION

Maternal ApoB levels and TG/HDL-C ratio could predict macrosomia occurrence in pregnancy, which might be a new target for early intervention to prevent excess fetal growth.

Effect of Elevated Ketone Body on Maternal and Infant Outcome of Pregnant Women with Abnormal Glucose Metabolism During Pregnancy

Ketone bodies are one of the products of fat metabolism which can be used as an alternative energy source for the human body in states of glucose deficiency. Normal pregnant women may develop ketosis due to physiological changes during pregnancy, while pregnant women with abnormal glucose metabolism are more likely to develop ketosis due to abnormal insulin secretion. Animal experiments and clinical studies have shown that exposure to high-ketone environments during pregnancy is closely related to adverse maternal and infant outcomes. However, there is no unified conclusion on whether ketone bodies should be routinely monitored during pregnancy. This review summarizes the existing studies on ketone body levels and pregnancy outcomes in the case of abnormal blood glucose during pregnancy, elaborates the current guidelines on the level of ketone bodies, provides the detection and treatment of ketosis in pregnant women with abnormal blood glucose in the clinical practice.