Experimental gerontology · 2026
Artificial intelligence-based whole-body skeletal muscle volume predicts long-term mortality after transcatheter aortic valve implantation.
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Clodi Nikolaus, Knapitsch Christoph, Hecke Gretha, Brandstetter Lucas, Scharinger Bernhard, Dinges Christian, Hammerer Matthias, Hoppe Uta C, Hergan Klaus, Schörghofer Nikolaos, Boxhammer Elke
Аннотация
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.
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.