Prediction of Cardiovascular Events Using Fully Automated Global Longitudinal and Circumferential Strain in Patients Undergoing Stress Cardiovascular Magnetic Resonance (CMR)
Authors' conclusion
The combination of rest-GLS and stress-GCS, with a cutoff of >-10% provided an incremental prognostic value over and above traditional prognosticators, including CMR parameters, for predicting MACE in patients undergoing stress CMR.
Study context
- Population
- Consecutive patients with known or suspected CAD referred for stress CMR, 2016–201896% feasible automated strain
- Participants
- 2,778 patients
- Exposure
- Fully automated rest-GLS and stress-GCS added to traditional prognosticators
- Outcome
- MACE:
- CV death
- Hospitalization for HF
- Follow-up
- Median 5.2 (4.8-5.5) years
Methodological detailssingle-centre, consecutive patients, Fully automated machine learning strain analysis
- Single-centre
- Consecutive patients
- Fully automated machine learning strain analysis
Main finding
As reported in the abstract
MACE · median 5.2 years
Hazard ratio (95% CI)P value
rest-GLS
1.09(1.05–1.13)
P < 0.001stress-GCS
1.08(1.03–1.12)
P < 0.001- Patients with major adverse clinical events (MACE)
- 316 (11.1%)
Observational data: associations, not effects.
Further findings
Incremental prognostic value of rest-GLS and stress-GCS cutoff >−10%
- C-index improvement
- 0.02
- Continuous net reclassification improvement
- 15.6%
- Integrative discrimination index
- 2.2%
- All P<0.001
Original publication
Prediction of Cardiovascular Events Using Fully Automated Global Longitudinal and Circumferential Strain in Patients Undergoing Stress CMR. Circ Cardiovasc Imaging. Published 15 September 2025. doi:10.
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