Improving models to predict cardiovascular disease in individuals with kidney dysfunction

A team led by Jiang He, MD, PhD and Joshua D. Bundy, PhD, MPH (Tulane University School of Public Health and Tropical Medicine) aimed to create new equations using many clinically available variables and novel biomarkers that may be especially important in patients with CKD. In the researchers' analysis of 2,604 participants of the Chronic Renal Insufficiency Cohort Study, they found that several factors not included in prior prediction models were important for atherosclerotic CVD prediction among patients with CKD. These included measures of long-term glycemia (HbA1c), inflammation (high-sensitivity C-reactive protein), kidney injury (urinary albumin-creatinine ratio), and cardiac injury (troponin-T and NT-proBNP).

"We created two new prediction tools for patients with CKD: the first is a simple model that includes factors routinely measured by healthcare providers and the second is an expanded model with additional variables particularly important to patients with CKD, including measures of long-term blood sugar, inflammation, and kidney and heart injury," said Dr. He. "We found that the new models are better able to classify patients who will or will not have a stroke or heart attack within 10 years compared with the standard models."