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Updated: Dec 2, 2025
Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million
Kunihiro Matsushita1, Simerjot K Jassal2, Yingying Sang1
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Insights
CKD Patch improves cardiovascular disease risk prediction by incorporating kidney function measures. This method enhances existing risk calculators, providing more accurate assessments for patients with chronic kidney disease.
Area of Science:
- Cardiology
- Nephrology
- Epidemiology
Background:
- Chronic kidney disease (CKD) measures like eGFR and albuminuria are crucial for cardiovascular disease (CVD) risk prediction.
- Current clinical guidelines lack standardized methods for integrating CKD measures into CVD risk prediction.
- CKD Patch offers a validated approach to calibrate and enhance CVD risk predictions using CKD measures.
Purpose of the Study:
- To develop and validate the CKD Patch method for improving CVD risk prediction.
- To incorporate estimated glomerular filtration rate (eGFR) and albuminuria into established risk calculators.
- To enhance the prediction of atherosclerotic CVD (ASCVD) and CVD mortality.
Main Methods:
- Developed "CKD Patches" using data from 4,143,535 adults across 35 datasets.
- Incorporated eGFR and albuminuria to enhance the Pooled Cohort Equation (PCE) for ASCVD and Systematic COronary Risk Evaluation (SCORE) for CVD mortality.
- Validated the CKD Patch approach in 4,932,824 adults from 37 independent datasets.
Main Results:
- CKD Patch significantly improved CVD mortality and ASCVD risk prediction in validation datasets (e.g., Δc-statistic 0.027 for CVD mortality, 0.010 for ASCVD).
- Risk prediction for CVD mortality was substantially increased in individuals with very high-risk CKD (2.64-fold), high-risk CKD (1.86-fold), and moderate-risk CKD (1.37-fold) compared to SCORE.
- Corresponding risk enhancements for ASCVD with PCE were 1.55-fold, 1.24-fold, and 1.21-fold, respectively, indicating underestimation by traditional methods in CKD patients.
Conclusions:
- CKD Patch provides a quantitative method to enhance ASCVD and CVD mortality risk prediction.
- This approach can be applied to risk equations recommended in major US and European guidelines.
- Utilizing CKD measures with CKD Patch leads to more accurate CVD risk assessments for patients with chronic kidney disease.
Background:
Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. "CKD Patch" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures.
Methods:
Utilizing data from 4,143,535 adults from 35 datasets, we developed several "CKD Patches" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch.
Findings:
We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018-0.036] and 0.010 [0.007-0.013] and categorical net reclassification improvement 0.080 [0.032-0.127] and 0.056 [0.044-0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89-3.40) in very high-risk CKD (e.g., eGFR 30-44 ml/min/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48-2.44) in high-risk CKD (e.g., eGFR 45-59 ml/min/1.73m2 with albuminuria 30-299 mg/g), and 1.37 (1.14-1.69) in moderate risk CKD (e.g., eGFR 60-89 ml/min/1.73m2 with albuminuria 30-299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37-1.81), 1.24 (1.10-1.54), and 1.21 (0.98-1.46).
Interpretation:
The "CKD Patch" can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available.
Funding:
US National Kidney Foundation and the NIDDK.
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