Cancer patients who suffer a heart attack are at a higher risk of further heart attacks due to their weakened cardiovascular system. The type of cancer can also affect the risk of bleeding, of arterial blood clotting, or both – requiring different treatments for prevention.
The new Artificial Intelligence tool, known as ONCO-ACS, uses machine learning to estimate a patient’s risk of death, major bleeding or recurrent cardiovascular events within six months of a heart attack, specifically tailored to people with cancer. It was developed by an international team of researchers led by the University of Leicester and reported in The Lancet.
Crucially, the research draws on the Virtual Cardio-Oncology Research Initiative (VICORI) – a national research platform for England that links routinely collected electronic health records from multiple sources. By capturing a patient’s journey across the NHS, researchers can explore how cancer and cardiovascular disease interact over time. VICORI is supported by the British Heart Foundation Data Science Centre (BHF DSC) and Health Data Research UK’s (HDR UK) Big Data for Complex Diseases programme, helping researchers securely access and analyse data at population scale.
Using this linked dataset, alongside comparable data from Sweden and Switzerland, researchers analysed outcomes for more than one million heart attack patients, including over 47,000 people with cancer. The scale and depth of the data allowed the team to train and validate an AI model that outperformed existing risk scores, which were not designed to account for the additional complexity introduced by cancer and its treatments.
People with cancer face a particularly difficult balance of risks after a heart attack. Until now, clinicians have lacked tools that reflect this complexity, often relying on risk models developed for the general population.
ONCO-ACS aims to address this gap by incorporating cancer-specific information alongside standard cardiovascular factors, supporting more personalised treatment decisions. Researchers hope the tool could help clinicians better identify patients who may benefit from more intensive monitoring or tailored therapies, while avoiding unnecessary risks for others.
Senior author Professor David Adlam, interventional cardiologist from the University of Leicester’s Department of Cardiovascular Sciences and member of the BDCD consortium, said:
“Significant advances in the management of heart disease and cancer alike have created new opportunities for these conditions to coexist. As a result, the growing overlap between cancer and heart attacks will confront cardiologists and oncologists with an increasingly complex patient population. We are addressing this pressing issue through a real-world data perspective.”
The VICORI dataset brings together cancer registry data from the National Cancer Registration and Analysis Service with cardiovascular audit data held by the National Institute for Cardiovascular Outcomes Research, alongside hospital admissions and mortality records. HDR UK’s role in supporting data infrastructure such as VICORI highlights the importance of secure, linked health data in enabling advanced data science and AI research.
By bringing together data from across the health system in privacy-protecting environments, HDR UK and its partners are helping unlock insights into complex diseases that could not be achieved using single datasets alone.
As AI tools like ONCO-ACS move closer to clinical use, access to high-quality, population-wide health data will remain central to improving care for people living with multiple long-term conditions.
Read more about about HDR UK’s Big Data for Complex Diseases programme.
