New heart disease calculator could save lives by identifying high-risk patients missed by current tools

Thursday, 18 April 2024

Experts have developed a new tool, called QR4, that more accurately predicts an individual's ten-year risk of cardiovascular diseases, like heart disease and stroke, particularly identifying high-risk patients that current prediction tools miss.

The new collaborative research, which is published today in Nature Medicine, is led by experts at the University of Oxford and co-led by Dr Carol Coupland, Professor of Medical Statistics in Primary Care, in the School of Medicine, at the University of Nottingham.

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 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.
  • 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,” said Dr Julia Hippisley-Cox, lead author, Professor of Clinical Epidemiology and General Practice, at the University of Oxford’s Nuffield Department of Primary Care Health Sciences. “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.”

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 syndrome significantly contribute to cardiovascular risk, underscoring the complex interplay between mental health, genetic factors, and heart health. Similarly, learning disabilities often coincide with barriers to effective health management and access, leading to an increased risk profile.”

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.

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."
Dr Carol Coupland, Professor of Medical Statistics in Primary Care at the University of Nottingham and co-author of the study

This new algorithm builds on almost two decades of work and leadership in the field of risk algorithms, and cardiovascular risk algorithms in particular. 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.

The full study can be found here.

Story credits

This research was a collaborative effort between researchers from the:

  • Nuffield Department of Primary Health Care Sciences, University of Oxford.
  • Centre for Academic Primary Care, School of Medicine, University of Nottingham, Nottingham.
  • King’s Centre for Lung Health, School of Immunology and Microbial Sciences, Faculty of Life Science and Medicine, King’s College London.
  • Usher Institute, University of Edinburgh.
  • Population Health Sciences, University of Bristol.
  • British Heart Foundation Centre of Research Excellence, Radcliffe Department of Medicine, University of Oxford.

The study analysed data from over 49.1 million person-years of follow-up to develop the model, during which 202,424 cardiovascular disease events occurred. To ensure the reliability of the QR4 algorithm, the researchers performed an external validation, which means they tested the algorithm's performance on a separate dataset that was not used in its development. This is an important step to assess how well the algorithm generalises to new data.

In the external validation, QR4 demonstrated superior performance compared to existing risk calculators. For instance, QR4 had a higher C-statistic than QRISK3 in both women (0.835 for QR4 vs. 0.831 for QRISK3) and men (0.814 for QR4 vs. 0.812 for QRISK3). A higher C-statistic indicates better ability to distinguish between those who will and will not develop cardiovascular disease.

Charlotte Anscombe - Media Relations Manager - Faculty of Medicine and Health Sciences
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