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

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: Participants had a mean age of 57 years; 55% were male and 40% were Black. Over 5 years, 512 developed PAD. Compared with an ABI-only model that included ABI, age, and sex (area under the receiver operating characteristic curve [AUC], 0.697; 95% CI, 0.688-0.713), a clinical model using routine variables showed similar discrimination (AUC, 0.682; 95% CI, 0.669-0.691; 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.