Key result
Low skeletal muscle volume linked to ~59% higher long-term mortality after TAVI.
Why the study?
Conventional skeletal muscle depletion assessment relies on single-slice measurements that do not capture total muscle burden in patients undergoing TAVI, whereas AI-based CT segmentation enables automated volumetric quantification of skeletal muscle and intermuscular fat.
Does low skeletal muscle volume assessed by AI-based CT volumetry predict all-cause mortality in patients undergoing TAVI?
Cohort (n=470)
Does low skeletal muscle volume assessed by AI-based CT volumetry predict all-cause mortality in patients undergoing TAVI?
Hazard Ratio: 1.589 (95% CI 1.101–2.294)
p-value: p=0.013
AI-derived low skeletal muscle volume from pre-procedural CT scans is an independent predictor of long-term mortality in patients undergoing TAVI, highlighting the prognostic importance of muscle quantity.
BACKGROUND: Frailty and sarcopenia are important determinants of outcomes in patients undergoing transcatheter aortic valve implantation (TAVI). Conventional skeletal muscle depletion assessment relies on single-slice measurements that do not capture total muscle burden. Artificial intelligence (AI)-based CT segmentation enables automated volumetric quantification of skeletal muscle and intermuscular fat (IMF). We investigated the prognostic value of AI-derived skeletal muscle volumetry and IMF in patients undergoing TAVI. METHODS: This retrospective cohort study analyzed pre-procedural CT scans of patients undergoing TAVI using AI-based segmentation (TotalSegmentator, Basel, Switzerland) to quantify skeletal muscle volume (SMV) and IMF. Patients were stratified by sex-specific quartiles. Skeletal muscle depletion was defined as the lowest SMV quartile (Q1), while muscle quality was assessed by the highest IMF quartile (Q4). The primary endpoint was all-cause mortality. RESULTS: A total of 470 patients were included. During a median follow-up of 4.56 years (IQR 3.57-5.99), 188 patients (40.0%) died. Patients with low SMV were older with lower body mass index and body surface area. Kaplan-Meier analysis demonstrated higher mortality in the lowest SMV quartile (log-rank p = 0.009). In multivariable Cox regression, low SMV remained independently associated with mortality (HR 1.589, 95% CI 1.101-2.294, p = 0.013). High IMF was not associated with mortality (HR 1.326, 95% CI 0.865-2.035, p = 0.196). In a combined model, SMV remained independently associated with mortality, whereas IMF remained non-significant. CONCLUSION: AI-based CT volumetry identifies low skeletal muscle volume as an independent predictor of long-term mortality after TAVI. Muscle quantity, rather than muscle quality, appears to be the dominant prognostic determinant. AI-driven body composition analysis may serve as an imaging biomarker of biological aging and reduced physiological reserve in older adults undergoing TAVI.
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Clodi et al. (2026) conducted a cohort in transcatheter aortic valve implantation (TAVI) (n=470). Low skeletal muscle volume (lowest quartile) vs. Higher skeletal muscle volume was evaluated on all-cause mortality (HR 1.589, 95% CI 1.101-2.294, p=0.013). Low skeletal muscle volume, quantified by AI-based CT volumetry, was independently associated with higher long-term all-cause mortality after TAVI (HR 1.589; 95% CI 1.101-2.294; p=0.013).
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