Related Experiment Video
Updated: Apr 26, 2026
Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation
Published on: June 3, 2018
Combining Coronary CTA Features with Clinical Risk Factors for Predicting Adverse Clinical Outcomes Following
He Zhu1, Yan Deng2, Shuo Zhang1
1Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China (H.Z., S.Z., X.P., P.N.).
Insights
Combining coronary computed tomography angiography (CCTA) imaging features with clinical data significantly improves the prediction of adverse outcomes following transcatheter aortic valve replacement (TAVR). This enhanced model offers better prognostic accuracy for TAVR patients.
Area of Science:
- Cardiology
- Medical Imaging
- Predictive Analytics
Background:
- Transcatheter aortic valve replacement (TAVR) is a crucial procedure for aortic stenosis.
- Predicting adverse outcomes post-TAVR is essential for patient management.
- Coronary computed tomography angiography (CCTA) provides detailed cardiac imaging.
Purpose of the Study:
- To evaluate the predictive value of CCTA-derived features combined with clinical factors for adverse events after TAVR.
- To develop and validate a combined predictive model for post-TAVR outcomes.
Main Methods:
- A multicenter cohort of 406 TAVR candidates with pre-procedure CCTA was analyzed.
- Major adverse cardiac events (MACE) and all-cause mortality were primary and secondary endpoints.
- Cox regression, C-index, and AUC were used to assess model performance; SHAP analysis explored feature importance.
Main Results:
- The combined model demonstrated superior predictive performance compared to the clinical model alone (C-index 0.756 vs 0.713).
- Independent predictors included age, serum creatinine, segment stenosis score (SSS), and left anterior descending (LAD)-fat attenuation index (FAI).
- LAD-FAI emerged as the most significant prognostic factor for MACE post-TAVR.
Conclusions:
- A combined model integrating CCTA features and clinical data enhances the prediction of adverse outcomes after TAVR.
- This approach offers improved prognostic accuracy for TAVR patients.
- CCTA-derived imaging biomarkers, particularly LAD-FAI, are valuable in risk stratification for TAVR.
Rationale And Objectives:
To explore the value of coronary computed tomography angiography (CCTA)-derived features combined with clinical factors for predicting adverse outcomes after transcatheter aortic valve replacement (TAVR).
Materials And Methods:
A multicenter cohort of 406 pre-CCTA TAVR candidates were enrolled. The primary endpoint was major adverse cardiac events (MACE), and the secondary endpoint was all-cause mortality. Univariable and multivariable Cox analyses were used to identify the independent clinical and CCTA-derived predictors for post-TAVR MACE, and the clinical model, the imaging model and the combined model were constructed. Model performance was evaluated with time-dependent area under the receiver operating characteristic (ROC) curve (time-dependent area under the curve (AUC)) and Harrell's concordance index (C-index). Likelihood ratio χ2 test was used to compare the nested models. The interpretability of the combined model was evaluated by using SHapley Additive exPlanations (SHAP) method.
Results:
The median follow-up was 768 days. MACE occurred in 12.6%, all-cause mortality in 13%. Univariable and multivariate analysis identified age (hazard ratio (HR) 1.082, P < 0.001) and serum creatinine (HR 1.010, P < 0.001) as independent clinical predictors, segment stenosis score (SSS, HR 1.077, P < 0.001) and left anterior descending (LAD)-fat attenuation index (FAI), HR 1.070, P < 0.001) as independent imaging predictors of MACE. The combined model outperformed the clinical model with higher C-index (0.756 vs 0.713), 1-year AUC (0.863 vs 0.779) and 3-year AUC (0.814 vs 0.760) in test cohort. Likelihood ratio test confirmed better fit of the combined model (P = 0.020). SHAP analysis showed LAD-FAI was the most important prognostic factor for post-TAVR MACE.
Conclusion:
The combined model incorporating CCTA features and clinical factors improves predictive efficacy for adverse outcomes in patients after TAVR.
More Related Videos
09:57Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement
Published on: January 20, 2022
09:32Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
Published on: December 9, 2021
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:28Imaging Studies for Cardiovascular System V: CT
01:22Aortic Regurgitation II: Clinical Features and Diagnostic Tests
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:30Coronary Artery Disease III: Clinical Manifestations
01:23Mitral Stenosis II: Clinical features and Diagnostic Tests