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Development and Validation of a Chronic Kidney Disease Prediction Model for Type 2 Diabetes Mellitus in Thailand

Wilailuck Tuntayothin1, Stephen John Kerr2, Chanchana Boonyakrai3

  • 1Department of Social and Administrative Pharmacy, Chulalongkorn University, Bangkok, Thailand.

Value in Health Regional Issues
|March 4, 2021
PubMed

Insights

This study developed two risk models to predict stage-3 chronic kidney disease (CKD) in Thai patients with type 2 diabetes mellitus (DM). The models accurately identify individuals at high risk, aiding clinical management and patient education.

Area of Science:

  • Nephrology
  • Endocrinology
  • Public Health

Background:

  • Type 2 diabetes mellitus (DM) is a leading cause of chronic kidney disease (CKD).
  • Predicting CKD progression in diabetic patients is crucial for timely intervention.
  • Thai patients with type 2 DM require specific risk assessment tools.

Purpose of the Study:

  • To identify predictors of stage-3 CKD in Thai patients with type 2 DM.
  • To develop and validate risk prediction models for stage-3 CKD.
  • To provide tools for clinical management and patient education.

Main Methods:

  • Retrospective cohort study of 2178 type 2 DM patients.
  • Cox proportional hazard regression for model development.
  • Model performance evaluated using discrimination (C statistic) and calibration tests.

Main Results:

  • 17.68% of patients developed stage-3 CKD during a median follow-up of 1.29 years.
  • Key predictors identified: age, male sex, urinary albumin to creatinine ratio, estimated glomerular filtration rate, and hemoglobin A1c.
  • Two 3-year risk models (laboratory and simplified) showed good predictive performance (C-statistics 0.890 and 0.812).

Conclusions:

  • Developed two validated 3-year risk models for stage-3 CKD in Thai type 2 DM patients.
  • Models demonstrate good discrimination and calibration.
  • These tools can enhance clinical decision-making and patient counseling for CKD prevention.
Abstract

Keywords:
Thailandchronic kidney diseasecohortprediction modelretrospectivetype 2 diabetes

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