Five-Year Risk Prediction Models for Peripheral Artery Disease in Patients With Chronic Kidney Disease

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Rationale & Objective: Patients with chronic kidney disease (CKD) have an increased risk of peripheral artery disease (PAD), yet no PAD risk-prediction models currently exist for this population. We developed and internally validated 5-year PAD risk-prediction models for individuals with CKD. Study Design: Prospective cohort study. Setting & Participants: 3,076 patients with CKD without PAD from the Chronic Renal Insufficiency Cohort (CRIC) study. Exposure: Clinically available variables, ankle-brachial index (ABI), and non-routinely measured cardiovascular disease biomarkers assessed at baseline. Outcomes: New-onset adjudicated clinical PAD event or an ABI ≤0.9 at an annual follow-up visit. Analytical Approach: Cox proportional hazards models were applied to estimate 5-year risk of incident PAD from baseline. Model performance was assessed by discrimination, calibration, and net reclassification improvement. All models were internally validated using Monte Carlo cross-validation. Results: <0.001). The biomarker-enhanced model achieved an AUC of 0.724 (95% CI, 0.711-0.745), which was not significantly different from the clinical model with ABI. Both the clinical model with ABI and the biomarker-enhanced model were well calibrated and improved reclassification of non-events compared with the ABI model (19.7%; 95% CI, 17.5-22.0% and 22.2%; 95% CI, 19.8-24.5%, respectively). Limitations: Lack of external validation. Conclusions: A PAD risk prediction model that combines clinical variables with ABI improves the identification of patients with CKD at high risk for PAD better than ABI alone.

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