Predicting and measuring mortality risk after transcatheter aortic valve replacement

Area of Science:

• Cardiology
• Interventional Cardiology
• Cardiac Surgery

Background:

• Transcatheter aortic valve replacement (TAVR) is a primary treatment for severe symptomatic aortic stenosis (AS).
• Accurate mortality prediction is essential due to the increasing volume of TAVR procedures.
• Existing risk models require evaluation for TAVR patient populations.

Purpose of the Study:

• To review existing surgical and TAVR-specific mortality prediction models (MPMs).
• To assess the performance of these models in derivation and validation cohorts.
• To discuss the role of adjunct risk factors in TAVR patient assessment.

Main Methods:

• Literature review of surgical and TAVR-specific mortality prediction models.
• Analysis of model performance in original and external validation cohorts.
• Discussion of non-model-based prognostic factors (frailty, imaging, biomarkers).

Main Results:

• Conventional surgical MPMs demonstrate suboptimal performance and miscalibration in TAVR populations.
• TAVR-specific MPMs show modest discriminative ability, often limited in external validation.
• Existing models do not fully meet the need for accurate post-TAVR mortality prediction.

Conclusions:

• There is a significant unmet need for robust, accurate TAVR mortality prediction models.
• Current TAVR-specific MPMs should be used cautiously alongside other prognostic factors.
• Heart teams must integrate frailty, echocardiography, CT imaging, and biomarkers for comprehensive TAVR risk assessment.