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ABSTRACT

We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies using a prospective international, multi-center cohort and identify useful predictive markers. Pregnant women with two live fetuses were enrolled at 11 + 0 to 13 + 6 weeks’ gestation and followed until delivery. GDM was diagnosed at 24–28 weeks’ gestation using the two-stage GCT and OGTT tests. Biochemical, biophysical, and blood assessments were conducted at three periods during pregnancy. Multiple machine learning models evaluated demographic, clinical, and laboratory parameters, including maternal factors (BMI, age, medical history), sonographic markers (crown rump length, estimated fetal weight, uterine artery pulsatility index), and blood and biochemical markers (placental growth factors, blood glucose, cell counts). LightGBM, XGBoost, and logistic regression models were compared using area under the curve (AUC) analysis. Among 596 women, 99 (16.6%) developed GDM. LightGBM demonstrated superior performance (AUC = 0.72, 95% CI 0.69–0.75). First-trimester high BMI was the strongest predictor, followed by elevated white blood cell counts and platelet levels. Detection rates (DR) were 28% and 42% at 10% and 20% false positive rates (FPR), respectively. Previous GDM was associated with an increased risk for GDM. GDM in twins is associated with certain characteristics of the first-trimester. Information from later trimesters has a limited impact. The GDM probability risk score increased with the severity of the treatment. An app to predict this score is available at: twin-pe.math.biu.ac.il.

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