Published in the prestigious journal eBioMedicine, part of The Lancet Discovery Science, this study demonstrates that this innovative model is as accurate as traditional clinical risk tools that require laboratory analyses and has the potential to be far more cost-effective and scalable. By relying solely on lifestyle and health-related questions, the model offers a simple, non-invasive solution that could revolutionize how high-risk individuals for CAD are identified and managed globally.
CAD, one of the leading causes of heart attacks, affects millions worldwide. In the Netherlands alone, 781.900 people live with CAD. Early risk prediction is vital to prevent life-threatening complications, such as heart failure, and to reduce the strain on healthcare systems.
“AI allows us to identify individuals at high risk for CAD earlier and more efficiently than ever before,” says Prof. Bruce H.R. Wolffenbuttel of the University Medical Center Groningen. “This means we can intervene sooner, improving outcomes for patients.”
Named QUES-CAD (Questionnaire-Based Evaluation for Estimating Coronary Artery Disease), the AI-powered tool forecasts a person’s 10-year CAD risk by analyzing answers about their lifestyle, medical history, and social factors. It eliminates barriers such as the need for trained medical staff or facilities for lab tests, making large-scale screenings feasible even in resource-limited or rural areas.
By applying QUES-CAD, healthcare providers can stratify the risk for thousands of CAD cases and significantly reduce unnecessary blood tests, enhancing efficiency and accessibility.
Researchers' next step is to test the model using data from clinical and first-line healthcare settings. This will validate QUES-CAD’s performance in real-world scenarios and further demonstrate its ability to identify high-risk individuals early.
Additionally, the AI model empowers healthcare providers to monitor at-risk groups closely, offering tailored lifestyle recommendations to prevent disease before it occurs. This proactive approach could save lives, alleviate pressure on healthcare systems, and lower costs significantly.
QUES-CAD’s development marks a major leap forward in CAD prevention and healthcare accessibility. Its ability to identify high-risk individuals with ease and precision could redefine population-wide screening efforts.
