Machine Learning Model to Predict Persistent Glucose Intolerance after Gestational Diabetes Mellitus: A Cross-Sectional Descriptive Study

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

TCTR

Registry ID

TCTR20251012001

Enrollment

335

Registered

2025-10-12

Start date

2026-05-19

Completion date

Unknown

Last updated

2026-08-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None listed

Interventions

Population - Target population: postpartum women with history of GDM - Sample population: postpartum women with history of GDM in Khon Kaen hospital

Prevention

Eligibility

Sex/Gender

Female

Age

18 Years to 50 Years

Inclusion criteria

Inclusion criteria: 1. Thai women 2. Older than 18-year-olds 3. History of GDM during pregnancy 4. Postpartum day 2-3

Exclusion criteria

Exclusion criteria: 1. Pregestational diabetes mellitus 2. Postpartum complication: infection, postpartum hemorrhage 3. Comorbid: hypertension, heart disease, coronary disease, seizure

Design outcomes

Primary

Measure	Time frame
Predictive model for identifying persistent glucose intolerance at one year postpartum of GDM women 1 year Collect from medical record at 2-3 day postpartum (PP) by nurse using case report form	—

Secondary

Measure	Time frame
Rate of impaired fasting glucose, diabetes mellitus, and normal at 2-3 day, 4-12 week, and 1 year postpartum 1 year Venous blood sampling at 1 year PP by nurse using FPG	—

Countries

Thailand

Contacts

Public ContactKamonwan Sansom

Khon Kaen Hospital

kamonwan.sansom@gmail.com0910641580

Outcome results