Dataset Information

Long-term Follow-up After Transcatheter Aortic Valve Replacement.

ABSTRACT:

Background

Transcatheter aortic valve replacement (TAVR) has become the standard of care in the majority of patients with symptomatic severe aortic stenosis. Data on long-term mortality and durability of transcatheter heart valves (THVs) beyond 5 years are limited. Our study aimed to assess elderly and high-risk patients' long-term outcomes treated with TAVR in a prospective single-centre registry focusing on the durability of THVs.

Methods

We included 795 patients with severe calcific aortic stenosis treated by transfemoral TAVR between 2006 and 2011. Echocardiography was performed at baseline; discharge; 1 year; and afterward, annually, until the longest available follow-up. Mortality rates were estimated for 1, 5, 6, 7, and 8 years. The rates of structural valve deterioration (SVD) and bioprosthetic valve failure (BVF) were assessed in accordance with consensus definitions. Outcome measures were adjudicated according to Valve Academic Research Consortium-2 (VARC-2).

Results

Median (interquartile range) follow-up time was 1345 (316; 2015) days. One-year, 5-year, 6-year, 7-year, and 8-year overall mortality was 25.4%, 59.0%, 64.6%, 67.9%, and 69.2%, respectively. At 8 years, no significant differences in mortality were found comparing self-expanding vs balloon-expandable valves (69.5% vs 68.0%, P = 0.709) and postdilatation (PD) vs no-PD (69.4% vs 69.2%, P = 0.363). SVD was detected in 26 patients (3.3%), and 19 (2.4%) of the 795 patients had evidence of BVF during follow-up.

Conclusions

Our study demonstrates good long-term results for high-risk patients who were alive up to 8 years after TAVR.

SUBMITTER: Haussig S

PROVIDER: S-EPMC8347830 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe approved use of transcatheter aortic valve replacement (TAVR) for aortic stenosis has expanded substantially over time. However, gaps remain with respect to accurately delineating risk for poor clinical and patient-centered outcomes. Our objective was to develop prediction models for 30-day clinical and patient-centered outcomes after TAVR within a large, diverse community-based population.MethodsWe identified all adults who underwent TAVR between 2013-2019 at Kaiser Permanente Northern California, an integrated healthcare delivery system, and were monitored for the following 30-day outcomes: all-cause death, improvement in quality of life, all-cause hospitalizations, all-cause emergency department (ED) visits, heart failure (HF)-related hospitalizations, and HF-related ED visits. We developed prediction models using gradient boosting machines using linked demographic, clinical and other data from the Society for Thoracic Surgeons (STS)/American College of Cardiology (ACC) TVT Registry and electronic health records. We evaluated model performance using area under the curve (AUC) for model discrimination and associated calibration plots. We also evaluated the association of individual predictors with outcomes using logistic regression for quality of life and Cox proportional hazards regression for all other outcomes.ResultsWe identified 1,565 eligible patients who received TAVR. The risks of adverse 30-day post-TAVR outcomes ranged from 1.3% (HF hospitalizations) to 15.3% (all-cause ED visits). In models with the highest discrimination, discrimination was only moderate for death (AUC 0.60) and quality of life (AUC 0.62), but better for HF-related ED visits (AUC 0.76). Calibration also varied for different outcomes. Importantly, STS risk score only independently predicted death and all-cause hospitalization but no other outcomes. Older age also only independently predicted HF-related ED visits, and race/ethnicity was not significantly associated with any outcomes.ConclusionsDespite using a combination of detailed STS/ACC TVT Registry and electronic health record data, predicting short-term clinical and patient-centered outcomes after TAVR remains challenging. More work is needed to identify more accurate predictors for post-TAVR outcomes to support personalized clinical decision making and monitoring strategies.