A Predictive Nomogram for Selective Screening of Chronic Kidney Disease: A Population-Based Study

Area of Science:

• Nephrology
• Public Health
• Epidemiology

Background:

• Chronic kidney disease (CKD) is a significant global health issue, linked to increased disability, reduced quality of life, and mortality.
• Many CKD cases are asymptomatic in early stages, leading to delayed diagnosis and treatment.
• Early detection and risk stratification are crucial for managing CKD progression and its complications.

Purpose of the Study:

• To determine the prevalence of CKD in the Vietnamese adult population.
• To develop a predictive model and nomogram for identifying individuals at risk of CKD.
• To provide insights for targeted screening strategies in Vietnam.

Main Methods:

• A cross-sectional, population-based study involving 1490 Vietnamese adults (533 men, 957 women).
• CKD diagnosis based on albumin-to-creatinine ratio and/or estimated glomerular filtration rate (eGFR) using CKD-EPI 2009 equation, following KDIGO 2012 guidelines.
• Bayesian Model Averaging was employed to identify the optimal predictive model for CKD.

Main Results:

• The overall prevalence of CKD was 13.1% (95% CI: 11.6-14.6%), with higher prevalence in women (13.8%) than men (11.8%).
• The optimal predictive model for CKD included age, hypertension, and diabetes.
• Key predictors were age (OR per 5-year increase: 1.19), hypertension (OR: 2.08), and diabetes (OR: 1.69). The model's predictive accuracy (AUC) was 0.7.

Conclusions:

• CKD is a common condition among Vietnamese adults.
• A simplified predictive model incorporating age, hypertension, and diabetes can effectively identify individuals requiring selective CKD screening.
• These findings support the development of targeted public health interventions for CKD prevention and management in Vietnam.