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Diagnostic performance of computed tomography-derived fractional flow reserve in patients with severe aortic stenosis undergoing transcatheter aortic valve implantation: a single-centre feasibility study.

Aug 2026 · Cardiovascular Journal of Africa · Vol 37 3, pp. 375-380 · 0 citations
Medicine

TL;DR

Computed tomography-derived fractional flow reserve (CT-FFR) provides good diagnostic performance for detecting haemodynamically significant CAD in TAVI candidates and integration into routine workflows may reduce purely diagnostic invasive procedures while maintaining accuracy.

Abstract

Objective

Coronary artery disease (CAD) is prevalent in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation (TAVI). Computed tomography-derived fractional flow reserve (CT-FFR) may provide non-invasive functional assessment using existing TAVI planning imaging to evaluate CT-FFR diagnostic performance compared with invasive coronary angiography (ICA) for identifying functionally significant CAD in TAVI candidates.

Methods

This single-centre retrospective study included 37 patients with severe AS undergoing TAVI (June 2016-June 2023). All underwent pre-procedural coronary CT angiography and ICA. CT-FFR was retrospectively analysed using deep learning-based software (threshold < 0.81 for significant stenosis). Diagnostic performance was assessed per-vessel (52 coronary arteries) using ICA as reference standard.

Results

Mean age was 79.2 ± 8.4 years; 56.7% were male. ICA identified significant CAD in 14 patients (37.8%), involving 21 lesions. CT-FFR successfully analysed all 52 vessels in the final cohort (mean value 0.86 ± 0.11) identifying 20 lesions (38.4%) as functionally significant; however, 8 of 62 initially screened patients (12.9%) were excluded due to insufficient CT image quality, reflecting a relevant limitation of clinical feasibility. CT-FFR demonstrated sensitivity 80.9% (95% CI: 58.1%-94.6%), specificity 93.5% (95% CI: 78.6%-99.2%), positive predictive value 85.0%, negative predictive value 90.6%, and diagnostic accuracy 88.5%. No significant difference existed between CT-FFR and ICA classifications (p > 0.05). At 30 days, no deaths, myocardial infarctions, or strokes occurred.

Conclusion

CT-FFR provides good diagnostic performance for detecting haemodynamically significant CAD in TAVI candidates. Integration into routine workflows may reduce purely diagnostic invasive procedures while maintaining accuracy. Larger prospective studies are needed to validate these findings.

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