A groundbreaking AI tool capable of predicting a person’s 10-year risk of fatal heart attacks could revolutionize the treatment of patients undergoing CT scans for chest pain. Research funded by the British Heart Foundation, unveiled at the American Heart Association’s Scientific Sessions in Philadelphia, reveals the tool’s potential to transform patient care.
The first real-world trial of this AI tool showcased its ability to enhance treatment for up to 45% of patients, potentially saving thousands of lives. By identifying individuals at risk of heart attacks who might not have received appropriate treatment, this technology aims to lower their risk effectively. Moreover, the cost-effectiveness of the tool suggests a paradigm shift in managing patients referred for chest pain investigations within the NHS.
In the UK, approximately 350,000 people undergo cardiac CT scans yearly to detect narrowings or blockages in coronary arteries. However, in three-quarters of cases with no clear signs of significant narrowings, patients are often discharged, unaware of potential future heart attacks due to undetectable narrowings that could become problematic if inflamed.
ANALYSIS
, Professor Charalambos Antoniades and the University of Oxford’s Radcliffe Department of Medicine conducted a comprehensive study analyzing data from over 40,000 individuals undergoing routine cardiac CT scans at eight UK hospitals. The study revealed that twice as many patients with no significant narrowings experienced heart attacks and cardiac deaths, highlighting the need for predictive tools.
Utilizing an AI tool trained with information on arterial inflammation and clinical risk factors, the team accurately predicted cardiac events among 3,393 additional patients over 7.7 years. Notably, individuals with high levels of arterial inflammation but no arterial obstructions exhibited over a ten-fold higher risk of cardiac death.
In a world-first pilot involving 744 patients, clinicians received AI-generated risk scores, prompting alterations in treatment plans for up to 45% of cases. The tool’s potential impact on early identification and preventative treatment for high-risk patients was evident, underscoring its immense value in guiding patient management.
COST-EFFECTIVENESS
Comparative analysis revealed the AI tool’s cost-effectiveness for the NHS, potentially leading to a substantial reduction in heart attacks, cardiac deaths, and strokes among those undergoing the test. With NHS England already commissioning the technology for a pilot program in five hospitals, the researchers anticipate nationwide implementation.
Professor Charalambos Antoniades emphasized the tool’s ability to identify high-risk heart patients, potentially altering treatment courses, and preventing avoidable deaths from heart attacks.
Professor Sir Nilesh Samani from the British Heart Foundation hailed the research’s significance, highlighting AI’s role in identifying at-risk patients and guiding treatment decisions, potentially saving numerous lives annually.
The promising strides of this AI tool could reshape cardiac care, offering hope in preventing unnecessary heart-related fatalities and guiding precise treatment for patients at risk.
