CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients
Authors' conclusion
CT-FFR was a strong independent predictor for MACE in diabetic cohort. The model incorporating CT-FFR, LAP and HbA1c yielded excellent performance in predicting MACE.
Study context
- Population
- Diabetic patients with intermediate pre-test probability of CAD referred for coronary CT angiography, followed ≥2 years; mean age 61.0 ± 7.0 years, 1031 malestraining cohort 957
- Participants
- 1,797 patients
- Exposure
- Model with computed tomography fractional flow reserve (CT-FFR) (model2) vs model with clinical and conventional imaging parameters (model1)
- Outcome
- MACE
- Follow-up
- At least 2 years
Methodological detailsprognostic model development, internal validation, independent external test cohort
- Prognostic model development
- Internal validation
- Independent external test cohort
Main finding
As reported in the abstract
Prediction of MACE (C-index), training cohort
model2
0.82
C-index · interval 0.77–0.87
model1
0.80
C-index · interval 0.75–0.85
model2 vs model1: P = 0.021 · model2 vs model1: P < 0.001
- Major adverse cardiac event (MACE) incidence during follow-up
- 7.18% (129/1797)
- Independent predictor of MACE: CT-FFR ≤ 0.80, HR
- 4.534 (p < 0.001)
- Independent predictor of MACE: HbA1c, HR
- 1.142 (p = 0.015)
- Independent predictor of MACE: low attenuation plaque, HR
- 3.973 (p = 0.041)
Observational data: associations, not effects.
Further findings
Model performance in internal validation (n=411) and external test (n=429) cohorts
- Similar findings in internal validation and external test cohorts
Original publication
CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients. Cardiovasc Diabetol. Published 21 March 2023. doi:10.
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