New Heart Risk Tool Reveals Hidden Ethnic Patterns
TOPLINE:
The American Heart Association's Predicting Risk of Cardiovascular Disease Events (PREVENT) equations successfully identified the risk for heart problems in a group of 361,778 ethnically diverse patients. Over a mean follow-up of 8.1 years, researchers observed 22,648 cardiovascular events, with the equations showing modest variation in performance across disaggregated ethnic subgroups.
METHODOLOGY:
The retrospective cohort study analyzed 361,778 primary care patients aged 30-79 years across the Sutter Health system in Northern California from January 2010 to September 2023, with participants requiring at least two primary care visits during the study period.
Participants were required to have several baseline data points for the PREVENT equations to evaluate, including non-high-density lipoprotein (HDL) cholesterol, systolic blood pressure, BMI, estimated glomerular filtration rate, diabetes status, and smoking status, all while being free of cardiovascular disease (CVD).
Primary outcomes included identifying CVD events, defined as total CVD, atherosclerotic CVD, and heart failure, using International Classification of Diseases, Ninth and Tenth Revision codes, with a mean follow-up duration of 8.1 years.
TAKEAWAY:
Among Asian populations, C statistics for total CVD ranged from a C statistic of 0.79 (95% CI, 0.77-0.81) in Filipino patients to a C statistic of 0.85 (95% CI, 0.83-0.87) in Asian Indian patients, with calibration slopes generally under 1.0, except for Asian Indian participants.
Hispanic subgroups showed consistent C statistics — a measure of how well a model distinguishes between two groups — between 0.80 and 0.82 for total CVD and good predictive performance.
The PREVENT equations outperformed the pooled cohort equations for predicting atherosclerotic CVD across all racial and ethnic groups and subgroups.
The researchers observed small differences in the performance of PREVENT equations for atherosclerotic CVD and heart failure among racial and ethnic groups and subgroups.
IN PRACTICE:
'Our results show that PREVENT equations performed well in this study cohort and similarly to the original equation development and validation cohort on the discrimination measure,' the researchers reported. 'In particular, the performance was slightly better in discriminating CVD events for Asian and Hispanic participants compared to Black or White participants in the study population. The equations slightly overestimated CVD risk for all three CVD event types in Asian and most Asian subgroups and accurately predicted CVD events among Hispanic and disaggregated Hispanic subgroups.'
'As the burden of CVD and its risk factors is forecasted to increase in the coming decades alongside rapid growth of the Asian and Hispanic populations in the US, the imperative for equitable clinical CVD prevention is more urgent than ever,' wrote Nilay S. Shah, MD, MPH, of Northwestern University Feinberg School of Medicine, in Chicago, in an editorial accompanying the journal article. 'Although best practices for clinical implementation of the PREVENT cardiovascular disease risk prediction models should be further investigated, [the new study shows] that the PREVENT equations are an important step forward for Asian and Hispanic communities that until now were unseen in CVD prevention recommendations.'
SOURCE:
The study was led by Xiaowei Yan, PhD, MS, MPH, of the Center for Health Systems Research at Sutter Health in Walnut Creek, California. It was published online on June 25 in JAMA Cardiology.
LIMITATIONS:
Despite disaggregation of Asian and Hispanic subgroups, the researchers were unable to fully examine other disaggregated groups due to small sample sizes. As a study based on data from a healthcare system, the population may be biased toward less healthy individuals compared to the general population. Almost half of eligible patients had incomplete data and were excluded from the analysis, potentially introducing selection bias.
DISCLOSURES:
The study received funding from the National Heart, Lung, and Blood Institute; the American Heart Association/Harold Amos Medical Faculty Development program; and the Doris Duke Foundation, as well as consulting fees from multiple organizations including Novartis, Novo Nordisk, Esperion Therapeutics, and others.
This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.
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