Artificial Intelligence Model to Predict Chronic Kidney Outcomes in a Vietnamese Cohort

NCT07751939 | Active Not Recruiting

• 1 other identifier: IRB-VN01002/IRB00010293
• Study Type: observational
• Target: 1,182
• Locations: 1 country
• Sites: 1

Brief Summary

Chronic kidney disease (CKD) is a common condition that imposes a substantial health burden and contributes significantly to global morbidity and mortality. In the United States, 2024 data indicate that about 14% of adults - more than 31 million people - have CKD, costing hundreds of billions of dollars each year. In Vietnam, the estimated prevalence is 12.8%, affecting roughly 10 million people. Because CKD often progresses silently, reliable early prediction of adverse outcomes - end-stage kidney disease (ESKD), disease progression, and death - carries considerable clinical value. Timely intervention in high-risk patients can improve quality of life and reduce morbidity, mortality, and the costs arising from kidney replacement therapy. Several statistical models predict CKD outcomes from variables such as age, sex, eGFR, and albuminuria. However, most were developed predominantly in White populations, and evidence for their generalizability to other ethnic groups, including Vietnamese, remains scarce. Some models omit proteinuria despite its strong prognostic role in CKD, and most do not account for therapies proven to slow progression, such as renin-angiotensin-aldosterone system (RAAS) inhibitors and sodium-glucose cotransporter-2 (SGLT2) inhibitors. Machine learning (ML), a branch of artificial intelligence, enables computers to learn latent patterns from data and make predictions without being explicitly programmed. Compared with traditional statistics, ML can represent complex, non-linear, and highly collinear relationships that conventional regression may miss, and has recently shown superior predictive performance across many clinical settings. Contemporary CKD care has advanced substantially: landmark trials have established the renal and cardiovascular benefits of SGLT2 inhibitors regardless of diabetes status, and current KDIGO guidance emphasizes risk-based, individualized management. Prediction models built before this therapeutic era may no longer capture current risk adequately. We therefore propose to develop an artificial intelligence-based model to predict CKD outcomes suited to the new treatment era in the Vietnamese population. Outcomes comprise disease progression (a ≥ 40% decline in eGFR or ESKD) and renal or cardiovascular death. Predictors are restricted to baseline comorbidities and routine blood and urine tests that are widely recommended for CKD monitoring. Using a prospective cohort, we will determine the 2-year incidence of these composite events, develop and compare several ML algorithms (logistic regression, random forest, decision tree, Naïve Bayes, k-nearest neighbours, and support vector machine...), benchmark them against existing equations (KFRE and CKD-PC), and select the optimal model, using SHAP-based interpretation to clarify each predictor's contribution. The minimum sample size of 1,182 was derived using the method of Riley.

Conditions

Chronic Kidney Disease

Keywords

Machine Learning; Artificial Intelligence; Prediction Model; eGFR; Vietnamese cohort

Outcome Measures

Primary Outcomes (1)

• Composite of CKD progression and renal or cardiovascular death

  Incidence of the composite outcome, defined as disease progression (a \>= 40% decline in eGFR or end-stage kidney disease \[ESKD\]) or renal or cardiovascular death.

  2 years

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients with chronic kidney disease managed at the University Medical Center Ho Chi Minh City.

You may qualify if:

• Age \>= 18 years.
• eGFR 20-60 mL/min/1.73 m2, stable for at least 3 months before enrolment.
• Provides written informed consent to participate.

You may not qualify if:

• Currently receiving kidney replacement therapy (haemodialysis, peritoneal dialysis, or kidney transplant).
• Acute illness at enrolment (acute infection, acute heart failure, or progressive liver disease).
• Current malignancy.
• Life expectancy \< 6 months.

Sponsors & Collaborators

• University Medical Center Ho Chi Minh City (UMC): lead

Study Sites (1)

University of Medicine and Pharmacy at HCMC

Ho Chi Minh City, Vietnam

Location

MeSH Terms

Conditions

Renal Insufficiency, Chronic

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 3, 2026
• First Posted: August 7, 2026
• Study Start: October 2, 2025
• Primary Completion (Estimated): June 30, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: August 7, 2026

Record last verified: 2026-07

Locations

VN (1)