Risk Prediction Model for In-Hospital Mortality After TAVR

Mar 09, 2016
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Authors:
Edwards FH, Cohen DJ, O’Brien SM, et al., on behalf of the Steering Committee of the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry.
Citation:
Development and Validation of a Risk Prediction Model for In-Hospital Mortality After Transcatheter Aortic Valve Replacement. JAMA Cardiol 2016;Mar 9:[Epub ahead of print].
Summary By:
Troy M LaBounty, MD, FACC

Study Questions:

What are predictors of in-hospital mortality after transcatheter aortic valve replacement (TAVR)?

Methods:

This study identifies predictors of mortality in 13,718 patients undergoing TAVR from the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy (STS/ACC TVT) Registry, with model validation in a subsequent cohort of 6,868 individuals.

Results:

In-hospital mortality occurred in 730 individuals (5.3%) treated with TAVR. The C statistic was 0.67 and 0.66 for discrimination in the development and validation groups, respectively. Independent predictors of mortality included age (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.06-1.20), glomerular filtration rate (OR, 0.93 per 5-U; 95% CI, 0.91-0.95), hemodialysis (OR, 3.25; 95% CI, 2.42-4.37), New York Heart Association class IV symptoms (OR, 3.34; 95% CI, 1.59-7.02), severe chronic lung disease (OR, 1.67; 95% CI, 1.35-2.05), nonfemoral access (OR, 1.96; 95% CI, 1.65-2.33), and increased procedural acuity categories (categories 2, 3, and 4 with OR 1.57, 2.70, and 3.34, respectively).

Conclusions:

This study creates a predictive model of in-hospital mortality in patients treated with TAVR, which is validated in a subsequent population. These results should inform assessment of risk in patients considered for TAVR.

Perspective:

As TAVR becomes widely used, one challenge has been identifying which patients are most and least likely to benefit from this procedure. This large registry identifies seven variables that have a good ability to discriminate risk of in-hospital mortality, and are validated in a subsequent cohort of patients. These results can help us understand which patients are at increased short-term risk of mortality, and improve patient selection for TAVR. We can look forward to future models from this registry that identify factors that predict 30-day and 1-year mortality.

Keywords: Cardiac Surgical Procedures, Glomerular Filtration Rate, Heart Failure, Heart Valve Diseases, Hospital Mortality, Lung Diseases, Renal Dialysis, Risk, STS/ACC TVT Registry, Transcatheter Aortic Valve Replacement


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