Measuring heart fat with AI improves cardiovascular risk prediction
· News-MedicalThe findings show that the volume of heart fat could be used independently to predict cardiovascular events. It significantly improved the overall accuracy of long-term risk prediction when combined with the coronary artery calcium score and the PREVENT equation, especially among patients in low-risk categories.
"Pericardial fat has been recognized as a marker of cardiovascular risk, but this study shows how we can now measure it automatically and use it to meaningfully improve risk prediction, especially in patients at borderline or intermediate risk where clinical decisions are often less clear," says Zahra Esmaeili, first author and researcher in the Department of Cardiovascular Medicine at Mayo Clinic. "This opens the door to more personalized prevention strategies."
Key findings:
- Nearly 10% of participants developed cardiovascular disease during follow-up.
- Higher fat volume around the heart was independently associated with increased risk of cardiovascular events, even after accounting for traditional risk factors and coronary calcium scores.
- Participants with the highest coronary fat volume had elevated risk across all coronary calcium levels.
- Adding coronary fat measurements improved the accuracy of predicting cardiovascular events beyond established models.
Coronary artery calcium scoring is widely used to assess cardiovascular risk. This study shows that additional information can be extracted from the same scan without extra testing or cost.
Researchers note that further studies will help determine how best to incorporate coronary fat measurement into routine clinical care and whether it can guide treatment decisions.
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