There isn’t one universal heart-risk calculator. The model your clinician uses depends on where you live, your age, and whether the question is about a heart attack in the next 10 years or cardiovascular disease over the next 30. The newer PREVENT equations add kidney, metabolic, and neighborhood data; SCORE2 is calibrated to European countries; QRISK3 includes a long list of diagnoses and medications; and the older ASCVD and Framingham models use a shorter list of traditional risk factors.
The differences are easy to miss because every model returns a percentage. This guide compares the major general-population models, the variables they use, the time horizon they cover, and the c-statistics reported in their original studies.
The reported c-statistics come from different cohorts, outcomes, and validation designs. They are useful context, not a head-to-head ranking.
The short version: which model should you use?
- United States: PREVENT is the current AHA model for adults ages 30–79 without known cardiovascular disease. The Pooled Cohort Equations (ASCVD Risk Estimator) remain widely used in older guidelines and clinical software.
- Europe: SCORE2 is designed for adults ages 40–69 without cardiovascular disease or diabetes. SCORE2-OP extends the framework to older adults.
- United Kingdom: QRISK3 is the model used in UK primary care for adults ages 25–84.
- If you want a longer view: PREVENT estimates 30-year risk for adults ages 30–59. Older Framingham-based tools also offer lifetime-risk estimates, but lifetime and 30-year risk are not identical outputs.
- If inflammation or family history is central: the Reynolds Risk Score adds hs-CRP and parental history, but it was developed in narrower US cohorts and is not the default US calculator.
What the major risk calculators actually include
| Model | Typical population and outcome | Inputs | Horizon | Reported c-statistic* |
|---|---|---|---|---|
| Framingham General CVD | US adults; broad CVD outcome | Age, sex, total cholesterol, HDL, systolic BP, BP treatment, smoking, diabetes | 10-year; lifetime variants exist | 0.76 |
| ASCVD / Pooled Cohort Equations | US adults 40–79; heart attack, coronary death, or stroke | Age, sex, race coefficient, total cholesterol, HDL, systolic BP, BP treatment, smoking, diabetes | 10-year | 0.71 |
| Reynolds Risk Score | Initially healthy US women ≥45 and men ≥50; MI, stroke, revascularization, or CV death | Age, sex, total cholesterol, HDL, systolic BP, smoking, diabetes, hs-CRP, parental premature MI | 10-year | 0.81 women; about 0.76 men |
| SCORE2 | European adults 40–69 without CVD or diabetes; fatal and nonfatal CVD | Age, sex, smoking, systolic BP, total cholesterol, HDL; country-risk calibration | 10-year | 0.739 derivation; 0.67–0.81 external validation |
| QRISK3 | UK primary-care adults 25–84; CVD | Age, sex, ethnicity, deprivation, smoking, BP, cholesterol/HDL ratio, BMI, diabetes, kidney disease, atrial fibrillation, rheumatoid arthritis, migraine, steroids, antipsychotics, HIV, erectile dysfunction, and other clinical factors | 10-year | 0.88 women; about 0.86 men |
| PREVENT | US adults 30–79 without known CVD; total CVD, ASCVD, and heart failure | Age, sex, non-HDL cholesterol, HDL, systolic BP, BP treatment, smoking, diabetes, BMI, eGFR; optional HbA1c, urine albumin/creatinine ratio, and social deprivation index | 10-year and 30-year | 0.794 women; 0.757 men (base CVD model) |
*C-statistics are the values reported in the cited derivation or validation studies. A value of 0.5 is no better than chance; 1.0 is perfect discrimination. Because the studies used different populations, follow-up, endpoints, and censoring methods, these numbers should not be treated as a league table.
The models are answering different questions
The ASCVD equations estimate a first atherosclerotic cardiovascular event over 10 years. SCORE2 estimates fatal and nonfatal cardiovascular disease over the same period, but it recalibrates the baseline hazard for European countries. QRISK3 estimates a broader primary-care CVD outcome and includes diagnoses that do not appear in US equations.
PREVENT broadens the question in two ways. It estimates total cardiovascular disease, including heart failure, as well as ASCVD. It also estimates 30-year risk for adults ages 30 to 59. The American Heart Association says PREVENT is validated for adults ages 30–79 without known cardiovascular disease and uses separate outcome-specific equations for total CVD, ASCVD, and heart failure. AHA PREVENT calculator
That longer horizon is useful because a 35-year-old can have high lifetime exposure to cholesterol or blood pressure while still having a low probability of a clinical event before age 45. A 10-year score can be numerically low without the underlying risk factors being harmless.
Why the input lists differ
The traditional core: age, cholesterol, blood pressure, smoking
Framingham, ASCVD, SCORE2, and PREVENT all rely on some version of this core. The variables are easy to measure and were available in the cohorts used to build the models.
The exact cholesterol input varies. ASCVD uses total cholesterol and HDL. SCORE2 uses the same two values. PREVENT uses non-HDL cholesterol and HDL, which captures cholesterol carried in LDL and other atherogenic particles without requiring an LDL estimate.
None of these core models directly measures ApoB, Lp(a), or hs-CRP. Those markers can reveal risk that a traditional calculator does not see. Our ApoB guide explains why particle number can differ from LDL cholesterol, and our hs-CRP analysis covers inflammatory risk.
PREVENT adds kidney, metabolic, and social variables
PREVENT includes BMI and estimated glomerular filtration rate in its base equations. Its optional full model can add hemoglobin A1c, urine albumin-to-creatinine ratio, and a social deprivation index. This is why PREVENT needs more information than the older Pooled Cohort Equations.
Those additions are not automatically “better” for every use. They can improve calibration in the population where the model was built, but a model can lose accuracy when transported to a different health system or country. External validation remains important.
QRISK3 includes the medical record
QRISK3 is the outlier in the comparison. Along with standard measurements, it includes ethnicity, a deprivation score, BMI, family history, chronic kidney disease, atrial fibrillation, rheumatoid arthritis, migraine, systemic lupus, severe mental illness, steroid use, antipsychotic use, HIV, erectile dysfunction, and blood-pressure variability, among other variables. Original QRISK3 study
That breadth makes QRISK3 clinically useful in the UK, but it also makes the model harder to reproduce from a simple blood panel. It is a primary-care record model, not a biomarker-only score.
Reynolds adds inflammation and family history
The Reynolds Risk Score was designed to test whether hs-CRP and parental history added information to traditional risk factors. In the original women’s validation cohort, the c-statistic was 0.81. A companion study evaluated men. Reynolds Risk Score in women, Reynolds Risk Score in men
Reynolds is useful conceptually even where it isn’t routinely used: it shows that a risk model can include a biomarker from a different biological pathway. It was developed in selected US cohorts, however, so its reported performance does not transfer automatically to everyone.
What is a c-statistic, and why can’t we rank these models from it?
The c-statistic measures discrimination: how often a person who experiences an event receives a higher predicted risk than a person who does not. A c-statistic of 0.5 is random ordering. A c-statistic of 0.8 means the model ranks the eventual event higher about 80% of the time across comparable pairs.
The number depends on the sample. If a study includes a wide spread of ages and risk factors, discrimination can look stronger than it does in a narrow, low-risk clinical population. It also depends on the endpoint: predicting heart failure, all CVD, or a hard coronary event are different tasks.
Calibration is separate. A model can rank people correctly but systematically predict risks that are too high or too low. PREVENT’s development paper reported good discrimination, but external studies have found that calibration and discrimination change across health systems and countries. The same is true for SCORE2 and QRISK3.
Can anything predict events better?
Yes, although the better predictors are usually more expensive, less widely available, or harder to use for routine screening. In the CAC Consortium, adding coronary artery calcium to the Pooled Cohort Equations raised the C-statistic to about 0.80 for cardiovascular death and 0.82 for coronary death, with the largest gains in people at borderline or intermediate calculated risk. CAC Consortium comparison Coronary CT angiography can go further by measuring plaque burden, stenosis, and high-risk plaque features: in the PROMISE trial, a CT-based classification had a C-statistic of 0.776 for a composite cardiovascular outcome, compared with about 0.63 for the clinical score alone and 0.68 after adding CAC. PROMISE CT angiography analysis
Those numbers do not mean that everyone should have a CT scan. The studies used different populations and endpoints, and the PROMISE participants had symptoms that prompted evaluation. More detailed imaging, repeated measurements, genetic scores, and machine-learning models may improve prediction further, but the gains are not automatically transferable to a healthy screening population or to better clinical outcomes.
There is no known universal “fundamental limit” of 0.81. In principle, a model with complete information about a person’s arteries, exposures, treatments, and competing illnesses could discriminate much better. In practice, plaque rupture is partly unpredictable, measurements are noisy, risk factors and treatments change over time, and non-cardiovascular deaths can prevent a cardiac event from occurring. These sources of uncertainty limit real-world prediction, but competing risks do not impose one fixed C-statistic ceiling.
Why a risk score is not the same as an artery scan
Every model in this comparison estimates future clinical events from risk factors. None directly measures whether plaque is already present. A coronary calcium scan measures calcified coronary plaque. Coronary CT angiography can see noncalcified plaque. Carotid and femoral ultrasound can detect plaque outside the heart.
That distinction explains why a young person can have visible plaque and a low 10-year score. The score is answering “How likely is an event soon?” The scan is answering “Is there disease in the artery now?” Neither answer replaces the other.
What to use in practice
Start with the model calibrated to your country and clinical setting. Use PREVENT in the US when your clinician has the required information, SCORE2 in Europe, and QRISK3 in the UK. Treat the resulting percentage as an estimate, not a diagnosis.
If the treatment decision is uncertain, risk-enhancing information can help: ApoB, Lp(a), hs-CRP, family history, kidney function, diabetes, and sometimes coronary calcium. The most useful next test is the one that could change what you do.
If you’re young, pay attention to the 30-year or lifetime view when available. A low 10-year number can reflect the short time window rather than a low lifetime burden of exposure.
The practical takeaway
Risk calculators are not competing heart-health thermometers. They are different prediction models, built in different countries, for different endpoints and time horizons.
PREVENT is the most expansive mainstream US model and the only one in this comparison that routinely offers 30-year estimates beginning at age 30. SCORE2 is designed for European calibration. QRISK3 uses the richest primary-care history. Reynolds demonstrates the value of adding hs-CRP and family history. ASCVD and Framingham remain important reference points because so many treatment thresholds were built around them.
The percentage is a useful starting point. It becomes more useful when you know what went into it, what it predicts, and what it leaves out.
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