New Information on Statins and Cardiovascular Risk – Asrar Qureshi’s Blog Post #1169

New Information on Statins and Cardiovascular Risk – Asrar Qureshi’s Blog Post #1169

Dear Colleagues! This is Asrar Qureshi’s Blog Post #1169 for Pharma Veterans. Pharma    Veterans Blogs are published by Asrar Qureshi on its dedicated site https://pharmaveterans.com. Please email to pharmaveterans2017@gmail.com  for publishing your contributions here.

Credit: Franki Frank

Credit: Pavel Danilyuk

Preamble

This blog post is based on a recent article published in JAMA - Journal of American Medical Association. Link to article at the end.

Rethinking Risk: How New Equations Could Alter Cardiovascular Prevention

Cardiovascular disease remains a leading cause of death worldwide. Deciding who should receive preventive therapy, like statins, depends heavily on risk estimation models. For years, clinicians have relied on the Pooled Cohort Equations (PCEs), developed in 2013, to estimate a person’s 10-year risk of atherosclerotic cardiovascular disease (ASCVD). But medical science evolves, populations change, and new data becomes available.

In 2023, the American Heart Association released updated risk equations called PREVENT (Predicting Risk of Cardiovascular Events). These models were designed to be more contemporary, more inclusive, and more precise. The JAMA Internal Medicine study by Anderson et al. examines how applying these new PREVENT equations, as opposed to the older PCEs, changes risk estimates and, critically, changes the number of people deemed eligible for primary prevention statin therapy in the United States.

The findings are paradigm-shifting, and they raise important questions for clinicians, public health authorities, and patients alike.

The Core Findings: Lower Risks, Fewer Statin Recommendations

Substantial Downward Revision of Risk Estimates

In a representative sample of U.S. adults aged 40 to 75 without existing cardiovascular disease, the authors found that using the PREVENT model, compared to the traditional PCEs, yielded significantly lower mean risk estimates across all demographic groups.

Mean 10-year ASCVD risk under PCEs: ~8.0%

Mean 10-year ASCVD risk under PREVENT: ~4.3%

The largest declines in estimated risk under PREVENT were seen in older adults (ages 70–75) and in Black individuals.

This suggests that the older PCEs may have overestimated risk for contemporary populations, perhaps because they were based on cohorts recruited in earlier decades, and did not reflect broader diversity or medical advances in prevention.

Fewer People Qualify for Primary Prevention Statins

Because risk estimates under PREVENT are lower, fewer people would meet the threshold for starting primary prevention statin therapy (i.e., using statins in people without overt cardiovascular disease). Using PCEs, 45.4 million U.S. adults are eligible by guidelines. The PREVENT model reduces this to 28.3 million, a drop of 17.3 million individuals.

Even more striking: among those currently taking statins, about 4.1 million no longer meet the eligibility criteria under PREVENT.

Reclassification Across Risk Categories

The shift isn’t just about “just eligible vs. not eligible.” Many individuals currently grouped as intermediate or high risk under PCEs would be reclassified as borderline or low risk under PREVENT. 

For example:

Of those labeled “intermediate risk” by PCEs (7.5–19.9%), a substantial portion would be downgraded to lower risk categories under PREVENT.

Among those labeled “high risk” under PCEs (>20%), many would shift to “intermediate risk” under PREVENT.

In aggregate, about 40.8% of adults would move to a lower risk category.

Importantly, these reclassifications occurred across age, sex, and racial groups. The trend of downward risk shift was consistent, not isolated to any one subgroup.

Why the Risk Estimates Changed: What’s Different in PREVENT

The PREVENT model was intentionally designed to improve upon the older PCEs. Key modifications include:

Exclusion of “race” as a variable

The PREVENT equations remove race from the risk equation, acknowledging that race is often a social construct with the potential to reinforce inequities.

Inclusion of kidney function (eGFR) and statin use

Kidney function is a known risk modulator for cardiovascular outcomes. PREVENT also factors in whether someone is already using statins, a relevant piece of information in modern cohorts.

Use of more modern and diverse datasets

PREVENT was derived from datasets including electronic health record (EHR) populations, larger, more contemporary, and more reflective of diverse patient populations—rather than older, narrower cohorts.

Optional variables for glycemic control and albuminuria

In its expanded version, PREVENT allows for incorporation of HbA1c and urinary albumin-to-creatinine ratio (UACR), factors with known links to cardiovascular risk.

These changes likely make PREVENT less prone to overestimation in lower-risk modern populations, and more sensitive to biologically meaningful risk markers (e.g., kidney function) than broader demographic proxies.

Benefits of Adopting PREVENT

Reduce overtreatment: Millions of people currently recommended statins under PCEs may not derive net benefit if their risk is overestimated. Reclassifying could reduce unnecessary medication use.

Personalization: The newer model allows finer risk stratification based on physiology (kidney function) instead of relying on demographic proxies.

Equity: Removing race as a variable may help avoid reinforcing racial disparities in treatment decisions, though this must be handled carefully to avoid under-recognizing higher-risk social determinants.

Rational resource allocation: Health systems can better target preventive therapy where it is most needed, instead of broadly spreading resources based on outdated models.

Sum Up

While this study is based on U.S. data, its lessons apply globally, especially in low- and middle-income countries where risk models often rely on older cohorts or models imported from high-income settings.

The transition from PCEs to the newer PREVENT risk equations represents not simply a tweak in calculators, but a potentially transformative shift in preventive cardiology. By estimating lower 10-year risks for many, the new models could reduce the number of people needing statin therapy. That change carries profound implications, for patients, clinicians, health systems, and public health modeling.

But the shift must be handled carefully: statistical innovation must be balanced against validation, transparency, equity, and patient communication. Models are tools—not masters. Shared decision-making, clear guidelines, and continual evaluation will determine whether PREVENT becomes a step forward in cardiovascular care, or a source of confusion and inconsistency.

In a world struggling with chronic disease burdens, the precision of our tools matters. Equations that better reflect current risk profiles can improve outcomes, reduce overtreatment, and redirect resources where they truly matter. The task now is to proceed thoughtfully, ensuring that innovation leads to better health—not just new numbers.

Concluded.

Disclaimers: Pictures in these blogs are taken from free resources at Pexels, Pixabay, Unsplash, and Google. Credit is given where available. If a copyright claim is lodged, we shall remove the picture with appropriate regrets.

For most blogs, I research from several sources which are open to public. Their links are mentioned under references. There is no intent to infringe upon anyone’s copyrights. If, any claim is lodged, it will be acknowledged and duly recognized immediately.

References: 

https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2819821