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Updated: May 12, 2026
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Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Five-Year Risk Prediction Models for Peripheral Artery Disease in Patients With Chronic Kidney Disease
Jing Chen1,2,3, Ling Tian4,5, Joshua D Bundy5
1Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX.
Kidney Medicine
|May 11, 2026
Summary
This study developed a peripheral artery disease (PAD) risk model for chronic kidney disease (CKD) patients. Combining clinical factors and ankle-brachial index (ABI) significantly improves PAD risk prediction in CKD.
Area of Science:
- Nephrology
- Cardiovascular Medicine
- Epidemiology
Background:
- Patients with chronic kidney disease (CKD) face a higher risk of peripheral artery disease (PAD).
- Existing PAD risk prediction models do not adequately address the CKD population.
- There is a critical need for validated tools to identify high-risk CKD individuals for PAD.
Purpose of the Study:
- To develop and internally validate 5-year PAD risk prediction models specifically for patients with CKD.
- To compare the predictive performance of models using clinical variables, ankle-brachial index (ABI), and cardiovascular disease biomarkers.
Main Methods:
- A prospective cohort study of 3,076 patients with CKD from the Chronic Renal Insufficiency Cohort (CRIC) study.
- Cox proportional hazards models were used to estimate 5-year PAD risk, incorporating clinical variables, ABI, and biomarkers.
- Internal validation was performed using Monte Carlo cross-validation to assess model performance (discrimination, calibration, reclassification).
Main Results:
- A model combining clinical variables and ABI demonstrated improved discrimination (AUC, 0.721) compared to an ABI-only model (AUC, 0.697).
- A biomarker-enhanced model showed similar performance (AUC, 0.724) to the clinical model with ABI.
- Both the clinical model with ABI and the biomarker-enhanced model showed good calibration and significantly improved risk reclassification.
Conclusions:
- A PAD risk prediction model integrating clinical variables and ABI enhances the identification of high-risk CKD patients.
- This combined model offers superior risk stratification for PAD compared to using ABI alone in the CKD population.
- Further external validation is needed to confirm the generalizability of these findings.
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