New QR4 model identifies hidden risk factors in cardiovascular disease

Collaborative research, led from the University of Oxford and published today in Nature Medicine, has developed a new tool called QR4 that more accurately predicts an individual's 10-year risk of cardiovascular diseases, like heart disease and stroke, particularly identifying high-risk patients that current prediction tools miss.

Key findings at a glance:

• QR4, a new cardiovascular risk prediction tool, was developed using health data from more than 16 million UK adults.
• QR4 includes seven new risk factors applicable to all adults: chronic obstructive pulmonary disease (COPD), learning disabilities, Down syndrome and four cancer types (blood, lung, oral and brain), highlighting how other significant diseases impact on heart health.
• QR4 also identified factors specific to women's health that were predictive of future heart disease risk, such as complication from high blood pressure during pregnancy, and postnatal depression.
• QR4 outperformed the most widely used heart disease risk calculators, including QRISK3 recommended by the National Institute for Health and Care Excellence (NICE) in the UK, accurately identifying more high-risk patients.
• Cardiovascular disease is a leading cause of death globally. Improved cardiovascular risk prediction will mean preventive treatments can be targeted to those most likely to benefit, getting the right treatments to the right people and reducing the overall burden of cardiovascular diseases to people and healthcare systems.

QR4 can be thought of as a type of calculator that lets doctors use an individual's health data, such as blood pressure, age and medical history, to estimate their likelihood of developing a cardiovascular disease over the next 10 years, allowing them to intervene early to prevent illness.

We believe that these findings are important to both patients and policy makers. They indicate that more groups of people are at increased risk for heart and circulatory diseases than previously recognised. These people could and should now benefit from treatments and other preventive measures to reduce their risk."

Dr. Julia Hippisley-Cox, lead author, Professor of Clinical Epidemiology and General Practice, University of Oxford's Nuffield Department of Primary Care Health Sciences

Better risk prediction for more people

The researchers identified several unique risk factors common to all genders that were not part of current cardiovascular risk calculators, as well as new factors specific to women's health such as pre-eclampsia, a pregnancy complication characterised by high blood pressure, and postnatal depression, that were predictive of future heart disease risk.

"While traditional cardiovascular risk factors such as smoking and high cholesterol are well-recognised, our latest research identifies less obvious, yet crucial risk indicators," said Professor Hippisley-Cox. "For instance, conditions like postnatal depression and Down's syndrome significantly contribute to cardiovascular risk, underscoring the complex interplay between mental health, genetic factors, and heart health. QR4 also helps to address inequalities in health, particularly around learning disabilities, which often coincide with barriers to effective health management and access, leading to an increased risk."

"Our findings also reveal that women with COPD are at a higher risk of cardiovascular diseases than previously understood," said Professor Mona Bafadhel, co-author, Chair of Respiratory Medicine, King's College, London and Asthma + Lung UK Professor. "This underscores the critical need for targeted cardiovascular monitoring and interventions in these patients, which begins with an early diagnosis."

Potential to reshape public health policies

By integrating these new predictors into the QR4 model, the researchers have developed a more nuanced and comprehensive tool for predicting cardiovascular disease, ensuring preventative strategies are more personalised, inclusive, and cater to the needs of a broader and more diverse population.

"Cardiovascular risk has been under-recognised in some populations, which through QR4 we can now better address," said Professor Keith Channon, BHF Professor of Cardiovascular Medicine at the University of Oxford. "The new findings will help identify more diverse groups of people who have high cardiovascular risk, enabling them to access interventions and treatments to reduce their risk."

QR4 gives the most accurate 10-year predications to date

As part of a comprehensive evaluation, the QR4 algorithm was rigorously tested against some of the most well-established cardiovascular risk assessment tools currently used in both the United States and Europe, improving on their predictive accuracy.

"The QR4 model potentially sets a new standard in cardiovascular risk assessment," said Dr Carol Coupland, senior researcher at the University of Oxford and Professor of Medical Statistics in Primary Care at the University of Nottingham and co-author of the study. "Our head-to-head comparisons with established models show that QR4 provides a more detailed risk profile for each individual, allowing for earlier and more precise intervention strategies. This is crucial in a field where early detection can save lives and could significantly impact how we approach prevention on a global scale."

This new algorithm builds on almost two decades of work and global leadership in the field of risk algorithms, and cardiovascular risk algorithms in particular. QR4's predecessor, QRISK is widely used in the NHS to assess over 5 million patients a year, often as part of NHS Health Checks.

The researchers hope that clinicians, advisory bodies, regulators, and health and care policymakers consider the implications of QR4's advanced predictive capabilities on national health guidelines and support its adoption in clinical settings to improve cardiovascular health outcomes.