Evidence map›Paper›PMID 42897525›Full record

ArticleBMC pregnancy and childbirth2026

A risk assessment model for gestational diabetes mellitus integrating maternal demographics and hematological indices in Thai women.

Thaveesak Sai-Ong, Donrawee Waeyeng, Tanaporn Khamphaya, Yanisa Rattanapan, Chutima Jansakun, Atsuko Ikeda, Supabhorn Yimthiang

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Article in BMC pregnancy and childbirth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Thaveesak Sai-OngEnvironmental, Safety Technology and Health Program, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand.
Donrawee WaeyengDepartment of Environmental Health, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand.
Tanaporn KhamphayaDepartment of Occupational Health and Safety, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand.
Yanisa RattanapanSchool of Allied Health Sciences, Walailak University, Nakhon Si Thammarat, Thailand.
Chutima JansakunSchool of Allied Health Sciences, Walailak University, Nakhon Si Thammarat, Thailand.
Atsuko IkedaCenter for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan.
Supabhorn YimthiangEnvironmental, Safety Technology and Health Program, School of Public Health, Walailak University, Nakhon Si Thammarat, Thailand. ksupapor@mail.wu.ac.th.

Funding

National Research Council of Thailand N41A670637
6 · The paper itself

Abstract

backgroundUniversal 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.

methodsWe 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.

resultsSix 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 × 10

conclusionThe 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.

Indexed as

Diabetes, GestationalAdultCross-Sectional StudiesFemaleHumansMaternal AgePregnancyRisk AssessmentRisk FactorsROC CurveThailandGestational diabetes mellitusMaternal demographicsPrediction modelPregnancy biomarkersThai pregnant women

Identifiers

PMID42897525
PMCPMC13678896

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