FP385PREDICTIVE MODELS FOR THE DEVELOPMENT OF PERIPHERAL ARTERY DISEASE AMONG PATIENTS WITH CHRONIC KIDNEY DISEASE

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INTRODUCTION: Patients with chronic kidney disease (CKD) have an increased risk of developing peripheral artery disease (PAD). In addition to ankle-brachial index (ABI), several predictors have been showed to associate with risk of PAD. We developed and validated predictive models for the development of PAD among patients with CKD. METHODS: Predictive models were developed using demographic, clinical, and laboratory data from 2,007 CKD patients without PAD in the Chronic Renal Insufficiency Cohort (CRIC) study and validated in 2,903 CKD patients without PAD from combined cohorts (the Multi-Ethnic Study of Atherosclerosis, the Framingham Offspring Study, and the Cardiovascular Health Study). CKD was defined as an estimated-glomerular filtration rate (eGFR) <60 mL/min/1.73m2. Predictive models were developed using multiple logistic regression analysis and evaluated using C-statistics and integrated discrimination improvement for discrimination, calibration plots and Akaike Information Criterion for goodness of fit, and net reclassification improvement (NRI) at five years. Incident PAD was defined as a newly onset clinical PAD event or an ABI ≤0.9 at one of examination visits during the five years of follow-up. RESULTS: Over five-years of follow-up, 416 PAD events occurred in the CRIC Study and 311 in the combined validation cohorts. The most accurate and clinical usable model included age, male gender, black race, ABI, eGFR, current smoking, history of diabetes, use of antidiabetic medication, history of cardiovascular disease, and systolic blood pressure (C statistic, 0.75; 95% confidence interval [CI], 0.72-0.78 in the development cohort and 0.71; 95% CI, 0.68-0.74 in the validation cohorts). In the validation cohorts, this model was more accurate in predicting PAD events within 5 years than a simpler model that included age, male gender, black race, ABI, and eGFR with reclassification for PAD (NRI, 8.18%; 95% CI, 2.54% to 13.82%). CONCLUSIONS: We have developed and validated predictive models using readily-available clinical and laboratory data which significantly improve prediction of PAD beyond ABI among CKD patients. These models could be used in clinical practice for risk classification and prediction.

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