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The following is a summary of “Risk prediction modeling for cardiorenal clinical outcomes in patients with non-diabetic CKD using US nationwide real-world data,” published in the January 2025 issue of Nephrology by Wanner et al.

Chronic kidney disease (CKD) affects over 840 million people and is linked to higher mortality, driven by cardiovascular risk and worsening kidney function. 

Researchers conducted a retrospective study to identify risk factors and develop prediction models for cardiorenal outcomes in patients with non-diabetic CKD.  

They included adults with non-diabetic CKD (stages 3 or 4) from the Optum® Clinformatics® Data Mart US healthcare claims database. About 3 outcomes were investigated: kidney failure/need for dialysis, hospitalization for heart failure, and worsening of CKD. Multivariable time-to-first-event prediction models were developed using swarm intelligence methods. Model discrimination was demonstrated by stratifying cohorts into 5 risk groups, with separation shown by Kaplan–Meier curves. 

The results showed that for kidney failure/need for dialysis, strong risk factors included stage 4 CKD (HR=2.05, 95% CI = 2.01–2.08), severely increased albuminuria-A3 (HR=1.58, 95% CI = 1.45–1.72), metastatic solid tumor (HR=1.58, 95% CI = 1.52–1.64), anemia (HR=1.42, 95% CI = 1.41–1.44), and proteinuria (HR=1.40, 95% CI = 1.36–1.43). For hospitalization for heart failure, major risk factors were history of heart failure (HR=2.42, 95% CI = 2.37–2.48), loop diuretics use (HR=1.65, 95% CI = 1.62–1.69), severely increased albuminuria-A3 (HR=1.55, 95% CI = 1.33–1.80), atrial fibrillation or flutter (HR=1.53, 95% CI = 1.50–1.56), and stage 4 CKD (HR=1.48, 95% CI = 1.44–1.52). For worsening CKD, stage 4 CKD (HR=2.90, 95% CI = 2.83–2.97), severely increased albuminuria-A3 (HR=2.30, 95% CI = 2.09–2.53), stage 3 CKD (HR=1.74, 95% CI = 1.71–1.77), polycystic kidney disease (HR=1.68, 95% CI = 1.60–1.76), and proteinuria (HR=1.55, 95% CI = 1.50–1.60) were the primary risk factors. Female gender and normal-to-mildly increased albuminuria-A1 were linked to lower risk in all models. 

Investigators developed risk prediction models using real-world data to identify individuals with non-diabetic CKD at high risk for adverse cardiorenal outcomes. The models demonstrated potential for broad clinical applications in patient care. 

Source: bmcnephrol.biomedcentral.com/articles/10.1186/s12882-024-03906-2