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Risk prediction modeling for cardiorenal clinical outcomes in patients with non-diabetic CKD using US nationwide
Christoph Wanner1, Johannes Schuchhardt2, Chris Bauer2
1Medizinische Klinik Und Poliklinik 1, Schwerpunkt Nephrologie, Universitätsklinik Würzburg, Würzburg, Germany. wanner_c@ukw.de.
Insights
New risk models identify patients with non-diabetic chronic kidney disease (CKD) at high risk for adverse cardiorenal outcomes. Stage 4 CKD, severe albuminuria, and anemia are key predictors for kidney failure and heart failure hospitalization.
Area of Science:
- Nephrology
- Cardiology
- Data Science
Background:
- Chronic kidney disease (CKD) affects over 840 million globally, increasing mortality and cardiovascular risk.
- CKD is associated with worsening kidney function and higher rates of cardiovascular events.
- Identifying risk factors for cardiorenal outcomes in non-diabetic CKD is crucial for patient management.
Purpose of the Study:
- To identify risk factors for adverse cardiorenal outcomes in non-diabetic CKD patients.
- To develop and validate risk prediction models for kidney failure, heart failure hospitalization, and CKD progression.
- To utilize routinely collected healthcare claims data for clinical risk assessment.
Main Methods:
- Adults with non-diabetic CKD stages 3-4 from a US claims database were analyzed.
- Multivariable time-to-event prediction models were developed using swarm intelligence.
- Model performance was assessed by stratifying patients into risk groups and analyzing Kaplan-Meier curves.
Main Results:
- Key predictors for kidney failure include stage 4 CKD, severe albuminuria (A3), metastatic solid tumor, anemia, and proteinuria.
- Major risk factors for heart failure hospitalization are history of heart failure, loop diuretic use, severe albuminuria (A3), atrial fibrillation, and stage 4 CKD.
- Worsening CKD is strongly associated with stage 4 CKD, severe albuminuria (A3), stage 3 CKD, polycystic kidney disease, and proteinuria.
Conclusions:
- Novel risk prediction models for cardiorenal outcomes in non-diabetic CKD have been developed.
- These models leverage readily available data from healthcare claims databases.
- The developed models show potential for widespread clinical application in patient care and risk stratification.
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.
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