BMC pregnancy and childbirth · 2026
A risk assessment model for gestational diabetes mellitus integrating maternal demographics and hematological indices in Thai women.
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Sai-Ong Thaveesak, Waeyeng Donrawee, Khamphaya Tanaporn, Rattanapan Yanisa, Jansakun Chutima, Ikeda Atsuko, Yimthiang Supabhorn
Аннотация
BACKGROUND: Universal screening for Gestational Diabetes Mellitus (GDM) is the gold standard, but it can be difficult and expensive in resource-limited settings. Many current prediction models rely on specialized tests that are not available in primary care. This study aimed to create a cost-effective risk assessment model using common maternal demographic and blood test data to help identify GDM risk in Thai pregnant women.
METHODS: We conducted a cross-sectional study with 200 pregnant women (24-28 weeks of gestation) at a secondary care hospital in Southern Thailand. We analyzed routine antenatal data, including complete blood count and other lab results. A risk-scoring system was built using multivariate logistic regression. We then tested the model's performance using Receiver Operating Characteristic (ROC) curve analysis to determine the optimal cut-off point.
RESULTS: Six independent predictors were found maternal age over 35 years, systolic blood pressure above 130 mmHg, glycosuria, hemoglobin at least 11 g/dL, platelets at least 260.50 × 103/µL, and neutrophil-to-lymphocyte ratio (NLR) of 2.89 or higher. The combined risk model performed better (AUC = 0.804; 95% CI: 0.74-0.87) than single-marker models. With a risk score cut-off of 2.5 (indicating three or more risk factors), the model had a sensitivity of 71.4% and a specificity of 78.1%.
CONCLUSION: The new risk assessment model, which uses basic demographic and routine blood test data, offers a reliable and affordable way to screen for GDM. This method enables early risk identification and helps healthcare providers use resources more efficiently, potentially reducing unnecessary tests in resource-limited settings.
METHODS: We conducted a cross-sectional study with 200 pregnant women (24-28 weeks of gestation) at a secondary care hospital in Southern Thailand. We analyzed routine antenatal data, including complete blood count and other lab results. A risk-scoring system was built using multivariate logistic regression. We then tested the model's performance using Receiver Operating Characteristic (ROC) curve analysis to determine the optimal cut-off point.
RESULTS: Six independent predictors were found maternal age over 35 years, systolic blood pressure above 130 mmHg, glycosuria, hemoglobin at least 11 g/dL, platelets at least 260.50 × 103/µL, and neutrophil-to-lymphocyte ratio (NLR) of 2.89 or higher. The combined risk model performed better (AUC = 0.804; 95% CI: 0.74-0.87) than single-marker models. With a risk score cut-off of 2.5 (indicating three or more risk factors), the model had a sensitivity of 71.4% and a specificity of 78.1%.
CONCLUSION: The new risk assessment model, which uses basic demographic and routine blood test data, offers a reliable and affordable way to screen for GDM. This method enables early risk identification and helps healthcare providers use resources more efficiently, potentially reducing unnecessary tests in resource-limited settings.