Use of KDIGO kidney health criteria better predicts CVD risk
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The inclusion of kidney health measures, as recommended by Kidney Disease: Improving Global Outcomes, led to more accurate predictions of 10-year risk for CVD events than did standard cardiovascular risk criteria alone.
“For the primary prevention of cardiovascular disease, a comprehensive evaluation using both traditional and non-traditional risk factors is important,” study co-author Weiqing Wang, MD, PhD, of Shanghai Jiaotong University School of Medicine, said in a related press release. “Evaluation using traditional risk factors such as glucose, blood pressure and lipids could make a first stratification on your risk, and further evaluation using non-traditional risk factors related to kidney health could significantly refine the stratification and predict the risk more accurately.”
To investigate how an assessment of kidney health might improve cardiovascular risk prediction, researchers added albumin-to-creatinine ratio (ACR) and eGFR measures (based on the 2012 KDIGO clinical practice guideline) to the commonly used atherosclerotic cardiovascular disease (ASCVD) risk score. Including 115,366 Chinese adults 40 years or older (with no history of CVD), researchers considered major CVD events (defined as nonfatal myocardial infarction, nonfatal stroke and cardiovascular death), and evaluated the improvement in prediction when ACR and eGFR were used individually, together or in combination with KDIGO risk categories.
Patients were followed for a mean of 415,111 person-years, during which time 2,866 major CVD events occurred (419 non-fatal myocardial infarctions, 1,812 non-fatal strokes and 635 cardiovascular deaths).
Researchers found risks for these events increased significantly across ACR and eGFR categories, and including the measures to a model with the ASCVD score improved both the prediction of CVD development and the reclassification of CVD risk after a participant experienced an event (net reclassification improvement was 4.78%).
Further findings indicated that combining ASCVD score with KDIGO risk categories led to more accurate CVD risk stratification.
“In combination, participants with an intermediate KDIGO risk and with a high or very high KDIGO risk were at significantly increased risks of developing major CVD events compared with participants with a low KDIGO risk in most ASCVD risk strata, including the stratum of an ASCVD risk score [of at least 20]%,” the researchers wrote. “In this stratum, participants with an intermediate KDIGO risk had a 42% increased risk of developing major CVD events and participants with a high or very high KDIGO risk had a 144% increased risk of developing major CVD events compared with participants with a low KDIGO risk.”
According to Wang and colleagues, these findings highlight the potential of using ACR and eGFR to improve prediction of CVD events, as demonstrated in a cohort of Chinese patients.
“Clinical applications of the KDIGO risk categories for cardiovascular disease prevention should be evaluated by interventional studies and in other ethnic populations,” they concluded.
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