Improving models to predict cardiovascular disease in individuals with kidney dysfunction
Washington, DC - A new study reveals that certain changes to models that predict the risk of developing cardiovascular problems in the general population can help clinicians improve risk prediction in individuals with kidney disease. The findings, which are published in JASN, may be used to help protect the heart health of patients with kidney disease.
Individuals with chronic kidney disease (CKD) often develop atherosclerotic cardiovascular disease (CVD)—such as strokes and heart attacks—but common prediction tools to determine a person's risk of CVD were developed for the general population and may not include certain factors relevant to patients with CKD. Improving CVD risk prediction in patients with CKD may help identify those among this growing population who are truly at high risk, as well as identify those who are at low risk and less likely to benefit from invasive interventions.
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).
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