Prevention & lipidsCohort study
AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging
- Multicentre cohort study
- 3 centres
- consecutive patients
Authors' conclusion
AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.
Study context
- Population
- Patients without known CAD undergoing SPECT/CT myocardial perfusion imaging
- Participants
- 7,613 patients
- Exposure
- Highest quartile vs Lowest quartile
- Outcome
- MACE:
- Death
- MI
- Unstable angina
- Revascularization
- Follow-up
- Median 2.4 years
Main finding
MACE · median 2.4 years (IQR: 1.3-3.4 years)
Hazard ratio
1.46 to 3.31
As reported in the abstract
- MACE occurred
- 1,045 (13.7%)
- Addition of all CT-based volumes and LV mass improved continuous net reclassification index
- 23.1%
Observational data: associations, not effects.
Further findings
Fully automated artificial intelligence (AI) processing time
- Coronary artery calcium (CAC)
- 6.2 ± 0.2 seconds
- Cardiac volumes and LV mass
- 15.8 ± 3.2 seconds
- average
Original publication
AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging. JACC Cardiovasc Imaging. Published 6 March 2024. doi:10.1016/j.jcmg.2024.01.006
Summarised from the published abstract; every number is checked against it