Risk prediction modeling for cardiorenal clinical outcomes in patients with non-diabetic CKD using US nationwide real-world data.

Abstract

Background: Chronic kidney disease (CKD) is a global health problem, affecting over 840 million individuals. CKD is linked to higher mortality and morbidity, partially mediated by higher cardiovascular risk and worsening kidney function. This study aimed to identify risk factors and develop risk prediction models for selected cardiorenal clinical outcomes in patients with non-diabetic CKD.

Methods: The study included adults with non-diabetic CKD (stages 3 or 4) from the Optum® Clinformatics® Data Mart US healthcare claims database. Three outcomes were investigated: composite outcome of kidney failure/need for dialysis, hospitalization for heart failure, and worsening of CKD from baseline. Multivariable time-to-first-event risk prediction models were developed for each outcome using swarm intelligence methods. Model discrimination was demonstrated by stratifying cohorts into five risk groups and presenting the separation between Kaplan-Meier curves for these groups.

Results: The prediction model for kidney failure/need for dialysis revealed stage 4 CKD (hazard ratio [HR] = 2.05, 95% confidence interval [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) as the strongest risk factors. History of heart failure (HR = 2.42, 95% CI = 2.37-2.48), use of loop diuretics (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) were the greatest risk factors for hospitalization for heart failure. 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 main risk factors for worsening of CKD stage from baseline. Female gender and normal-to-mildly increased albuminuria-A1 were found to be associated with lower risk in all prediction models for patients with non-diabetic CKD stage 3 or 4.

Conclusions: Risk prediction models to identify individuals with non-diabetic CKD at high risk of adverse cardiorenal outcomes have been developed using routinely collected data from a US healthcare claims database. The models may have potential for broad clinical applications in patient care.