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Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
Published on: June 7, 2024
Deep Learning-Derived Body Composition Analysis Predicts Long-Term Mortality After Transcatheter Aortic Valve
Chia-Hao Liu1,2, Agata Sularz1, Ghasaq Saleh1
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
Insights
Reduced skeletal muscle and adipose tissue after transcatheter aortic valve replacement (TAVR) are linked to higher mortality. Body composition analysis from CT scans can improve risk assessment for TAVR patients.
Area of Science:
- Cardiovascular Imaging
- Medical Physics
- Geriatric Medicine
Background:
- Transcatheter aortic valve replacement (TAVR) is a key treatment for aortic stenosis.
- Patient outcomes after TAVR can be influenced by various factors, including body composition.
- Preprocedural assessment is crucial for optimizing TAVR success.
Purpose of the Study:
- To investigate the association between body composition metrics from computed tomography (CT) angiography and all-cause mortality post-TAVR.
- To determine if specific body composition parameters can predict long-term survival after TAVR.
Main Methods:
- Utilized CT angiography data from 2642 TAVR patients (2011-2023).
- Quantified skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and SM index (SMI) using a deep learning model.
- Assessed 3-year all-cause mortality using multivariable Cox proportional hazards models.
Main Results:
- Lower SM, SAT, VAT, and SMI were independently associated with increased 3-year all-cause mortality.
- Specific thresholds for these metrics indicated higher mortality risk (e.g., SM <128 cm², SMI <41 cm²/m²).
- Adjusted hazard ratios indicated significant associations for all analyzed body composition parameters.
Conclusions:
- Diminished skeletal muscle and adipose tissue reserves are significant independent predictors of mortality after TAVR.
- Automated CT-derived body composition analysis offers a promising tool for preoperative risk stratification in TAVR candidates.
- These findings can aid in refining clinical decision-making for patients undergoing TAVR.
Objective:
To examine the association between body composition metrics derived from preprocedural computed tomography (CT) angiography and all-cause mortality after transcatheter aortic valve replacement (TAVR).
Patients And Methods:
We included patients who underwent TAVR between September 1, 2011 and November 30, 2023 at a single academic center. Skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue areas (cm2), as well as SM index (SMI; cm2/m2), were quantified from CT angiography using a validated U-Net-based deep learning model. Associations between each parameter and 3-year all-cause mortality were assessed using multivariable Cox proportional hazards models adjusted for clinical covariates, with adjusted hazard ratios (aHRs) expressed per 1-SD increase.
Results:
Among 2642 patients (median age, 80.0 years [interquartile range, 74.0-85.0 years]; 1572 were men [59.5%]), median follow-up was 2.8 years, and 74.8% survived to 3 years. Lower SM, SAT, VAT, and SMI (analyzed as continuous variables) were independently associated with higher 3-year all-cause mortality (SM: aHR, 0.831; 95% CI, 0.762-0.906; SAT: aHR, 0.847; 95% CI, 0.775-0.926; VAT: aHR, 0.826; 95% CI, 0.762-0.896; SMI: aHR, 0.832; 95% CI, 0.763-0.907; all P≤.001). Restricted cubic spline analysis showed increased mortality risk below threshold values of the following-SM<128 cm2, SAT<161 cm2, VAT<104 cm2, and SMI<41 cm2/m2; sex-specific thresholds were also derived.
Conclusion:
Reduced SM and adipose tissue reserves are independently associated with increased mortality after TAVR. Automated CT-derived body composition assessment may improve preoperative risk stratification and guide clinical decision making in TAVR candidates.
