Aims: Transcatheter aortic valve implantation (TAVI) is an increasingly common procedure in elderly and multimorbid patients with aortic stenosis. We aimed at developing a pre-procedural risk evaluation scheme beyond current surgical risk scores.
Methods: We developed a risk algorithm for 1-year mortality in two cohorts consisting of 845 patients undergoing routine TAVI procedures by commercially available devices, mean age 80.9 ± 6.5, 51 % women. Clinical variables were determined at baseline. Multivariable Cox regression related clinical data to mortality (n = 207 deaths).
Results: To account for variability related to age and sex and by enrolment site we forced age, sex, and cohort into the score model. Body mass index, estimated glomerular filtration rate, hemoglobin, pulmonary hypertension, mean transvalvular gradient and left ventricular ejection fraction at baseline were most strongly associated with mortality and entered the risk prediction algorithm [C-statistic 0.66, 95 % confidence interval (CI) 0.61-0.70, calibration χ (2)-statistic = 6.51; P = 0.69]. Net reclassification improvement compared to existing surgical risk predication schemes was positive. The score showed reasonable model fit and calibration in external validation in 333 patients, N = 55 deaths (C-statistic 0.60, 95 % CI 0.52-0.68; calibration χ (2)-statistic = 16.2; P = 0.06). Additional measurement of B-type natriuretic peptide and troponin I did not improve the C-statistic. Frailty increased the C-statistic to 0.71, 95 % CI 0.65-0.76.
Conclusions: We present a new risk evaluation tool derived and validated in routine TAVI cohorts that predicts 1-year mortality. Biomarkers only marginally improved risk prediction. Frailty increased the discriminatory ability of the score and needs to be considered. Risk algorithms specific for TAVI may help to guide decision-making when patients are evaluated for TAVI.