Chronic kidney disease (CKD) patients may be at increased risk for atherosclerotic cardiovascular disease, but no ASCVD risk prediction models are currently in place to inform clinical care and prevention strategies, Joshua Bundy, PhD, of Tulane University, New Orleans, and colleagues wrote in their paper, published in the
To improve the accuracy of ASCVD risk prediction, the researchers developed several models using data from the Chronic Renal Insufficiency Cohort (CRIC) study. This longitudinal cohort study included more than 2,500 adult CKD patients. The participants’ ages ranged from 21-74 years, with the mean age having been 55.8 years, and 52.0% of the cohort was male.
Kidney function was defined using the glomerular filtration rate; the mean estimated glomerular filtration rate (eGFR) of the study participants was 56.0 mL/min per 1.73m2. The primary endpoint for the prediction models was incident ASCVD, defined as a composite of incident fatal or nonfatal stroke or MI.
A total of 252 incident ASCVD events occurred during the first 10 years of follow-up from baseline (1.9 events per 1,000 person-years). Patients with ASCVD events were more likely to be older, Black, and current smokers. They also were more likely than those who did not experience ASCVD events to have less than a college level education, to have a history of diabetes, and to use blood pressure–lowering medications.
“In our study, we created two new prediction tools for patients with CKD: the first is a simple model that includes factors routinely measured by health care 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,” he explained. “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. The new tools may better assist health care providers and patients with CKD in shared decision-making for prevention of heart disease.”
The area under the curve for a prediction model using coefficients estimated within the CRIC sample was 0.736. This represented an accuracy higher than the American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE), which have shown an AUC of 0.730 (P = .03). The PCE were developed by the ACC and the AHA in 2013 to estimate ASCVD risk in the primary prevention population.
The second CRIC model that was developed using clinically available variables had an AUC of 0.760. However, the third CRIC biomarker-enriched model was even more effective, with an AUC of 0.771 – significantly higher than the clinical model (P = .001).
Model 1 included the ACC/AHA PCE variables with coefficients recalculated in the CRIC study sample. Model 2 (the CRIC Clinical Model) included age, HDL cholesterol, systolic BP, current smoking, urinary albumin-to-creatinine ratio (ACR), hemoglobin A1c, and hemoglobin. Model 3 (the CRIC Enriched Model) included age, total cholesterol, HDL cholesterol, current smoking, urinary ACR, A1c, apolipoprotein B, high-sensitivity C-reactive protein (hsCRP), troponin T, and N-terminal of the prohormone brain natriuretic peptide (NT-proBNP).
Both the clinical and biomarker models improved reclassification of non-ASCVD events, compared with the PCEs (6.6% and 10.0%, respectively).
Several factors not included in prior prediction models were important for atherosclerotic CVD prediction among patients with CKD, the researchers noted. These included variables routinely measured in clinical practice as well as biomarkers: measures of long-term glycemia (A1c), inflammation (hsCRP), kidney injury (urinary ACR), and cardiac injury (troponin T and NT-proBNP).
Patients who had an ASCVD event had higher levels of A1c, systolic and diastolic BP, urinary ACR, troponin T, and NT-proBNP; these patients also had lower levels of HDL cholesterol, eGFR, and hemoglobin, compared with those who did not have an event.
The study findings were limited by several factors including the selection of study participants based on a single assessment of kidney function, who had an above average baseline ASCVD risk, the researchers noted. Other limitations included the inability to include imaging variables in the models, and the overestimated risk in the highest predicted probability groups in the CRIC study.
However, the models significantly improve prediction beyond the ACC/AHA PCE in patients with CKD, they concluded.