Improving Models to Boost Prediction of CVD Issues in Patients With CKD

Adding risk factors specific to patients with chronic kidney disease (CKD) to risk prediction models for cardiovascular disease (CVD) could improve risk prediction capabilities for a population of patients often subject to invasive diagnostic procedures, investigators concluded.

Investigators found that implementing changes for prediction models for the risk of developing cardiovascular problems in the general population could help improve risk prediction in patients with chronic kidney disease (CKD)1.

The prospective longitudinal cohort study, published in the Journal of the American Society of Nephrology, supported the use of CKD-specific tools to predict the long-term risks of experiencing atherosclerotic cardiovascular disease (ASCVD) in patients with CKD, a population with different risk profiles compared with the general population.

“The new tools may better assist healthcare providers and patients with CKD in shared decision-making for the prevention of heart disease,” said Joshua D. Bundy, PhD, MPH, one of the lead study authors and an epidemiologist at Tulane University School of Public Health and Tropical Medicine, in a statement2.

Patients with CKD often have to undergo invasive diagnostic procedures because they are at an increased risk of cardiovascular disease. However, there are no ASCVD risk prediction models specifically developed for patients with CKD nor is it clear whether incorporating additional novel risk factors will improve ASCVD risk prediction. The models that do exist have only been designed for risk prediction for the general population.

“The availability of accurate ASCVD risk prediction equations for individuals with CKD could be informative for patients and clinicians, and improve shared decision-making for preventive therapy to reduce ASCVD incidence and mortality,” the investigators wrote.

The investigators developed and validated 10-year ASCVD risk prediction models in patients with CKD who did not have cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. The models were developed by using clinically available variables and novel biomarkers. Overall, 2604 participants with a mean age of 55.8 years were included in the analysis, 52.0% of whom were male. Additionally, 41.6% of the cohort had diabetes.

During the 10-year follow-up period, there were 252 incident ASCVD events (11.9 events per 1000 person-years). Participants who had an ASCVD event tended to be older, Black, have less than a college degree, be current smokers, have diabetes, and use blood pressure-lowering medications compared to patients who did not experience and ASCVD event.

Two predictions tools were created during the study. One was a simple model including factors routinely measured by providers and the other was an expanded model that included additional factors important to CKD, including:

  • Long-term blood sugar
  • Inflammation
  • Kidney injury
  • Heart injury

The finding showed that a model developed using clinically available variables and a model that is biomarker-enriched significantly improved discrimination and calibration beyond the original cardiovascular risk prediction models for the general population and significantly improved reclassification of non-ASCVD events.

Additionally, factors not included in prior prediction models, such a measures for long-term glycemia, inflammation, kidney injury, and cardiac injury, were deemed important for the accurate atherosclerotic cardiovascular disease prediction among the CKD population.

“For those with predicted risk of 20% or more, it may be prudent to conduct further evaluation, such as coronary artery calcium scoring. In addition, our models could be used in future clinical trials to more accurately recruit high risk patients, allowing for smaller sample sizes and more efficient conduct of trials,” the investigators noted.

The potential for adequate discrimination and calibration of the models, a possible overestimation of ASCVD risk in the highest predicted probability groups were listed as study limitations. The investigators said that future analyses should externally validate and refine their models among large and diverse groups of patients with CKD.

Reference

  1. Bundy K, Rahman M, Matsushita K, et al. Risk prediction models for atherosclerotic cardiovascular disease in patients with chronic kidney disease: The CRIC study. J Am Soc Nephrol. Published online February 10, 2022. Accessed on February 9, 2022. doi: 10.1681/ASN.2021060747
  2. Improving models to predict cardiovascular disease in individuals with kidney dysfunction [news release]. Washington, DC: The American Society of Nephrology; February 10, 2022.eurekalert.org/news-releases/942464?. Accessed February 9, 2022.