Disease's type
GDM
Experimental grouping
Randomly divided into a modeling cohort (GDM=1225,NGDM=2975) and validation cohort (GDM=519,NGDM=1281)
GPT's summary
This study developed and validated an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester indicators. Data from 6,000 pregnant women were analyzed, with independent risk factors identified as age, pre-pregnancy BMI, HbA1c, blood uric acid (UA), triglycerides (TG), and HDL-C. The model equation demonstrated good predictive performance, with an AUC of 0.803 in the modeling cohort and 0.782 in the validation cohort, along with satisfactory sensitivity and specificity. The results suggest that integrating clinical and laboratory indicators in early pregnancy can effectively predict GDM risk, supporting early screening, monitoring, and intervention for high-risk women.
RF's name
Triglyceride
Sample's type
Serum
Gestational weeks
8th to 12th gestational weeks
Experiemental methods
Glycerol-3-phosphate Oxidase-PAP Method
Machine learning algorithms
Logistic Regression
Title
Establishment of gestational diabetes risk prediction model and clinical verification
Evidence's type
Risk factor
Year
2024
Journal
Journal of Endocrinological Investigation
PMID