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OUR MISSION: TO REDUCE THE BURDEN OF CARDIOVASCULAR DISEASE
List of Authors:
Olaf Wendler, Gerhard Schymik, Thomas Walther, Dominique Himbert, Thierry Lefèvre, Hendrik Treede, Holger Eggebrecht, Paolo Rubino, Iassen Michev, Martyn Thomas
The SOURCE-Registry identified certain demographic variables as predictors of 30-day mortality after transcatheter aortic valve implantation (TAVI). However, preoperative variables to predict 1-year mortality are unknown at present. We analyze the 1 year mortality of patients enrolled in the SOURCE Registry in an attempt to develop a risk score which predicts mid-term outcome after TAVI.
The SOURCE-Registry collects data of patients treated at European centers following commercialization of the Edwards SAPIENTM bioprosthesis. Only data from centers that could provide all of their consecutively treated patients were included in the study. This univariate and multivariate analysis is based on Cohort I patients (n=1038), included from November 2007 to January 2009.
Out of the total of 1038 patients, 55.4% were treated using transapical (TA) TAVI and 44.6% with a transfemoral (TF) approach. One-year survival data are available on 98% of the patients.
The one-year Kaplan-Meier survival was 72.1% in TA and 81.1% in TF. Mortality between 30-days and 1-year were due in the majority to non-cardiac causes (49.2%) such as pulmonary, renal failure, cancer, stroke and gastrointestinal.
In proportional hazards multivariable analysis logistic EuroSCORE, liver disease, renal failure, smoking and “other cardiovascular” conditions were all predictors of inferior 1-year survival. The hazard ratio is 1.2 for a 10 point increase in EuroSCORE, 3.1 for patients with baseline liver disease, and 1.7 for patients with baseline renal failure.
Carotid artery stenosis, hypercholesterolemia and hypertension are also in the mathematical model, and work in a counterintuitive direction.
The 1-year survival after TAVI is convincing and in both implant approaches has improved compared to previously published early series. Although logistic EuroSCORE, liver disease and renal failure are strong predictors for 1-year mortality after TAVI, an appropriate risk score for TAVI is not available at present. Improvement on the data is being developed at present which hopefully will allow us to create a specific risk score for TAVI in the future.
Development of a Risk Score for TAVI
In the talk Olaf Wendler and colleagues presented the 1-year outcomes from over 1000 patients who had undergone percutaneous or transapical valve replacement for severe aortic stenosis using the Edwards Sapien Device. Data were collected in the SOURCE REGISTRY, which to date is the most comprehensive database on this topic. The aim of this analysis was to develop a dedicated risk score for predicting mortality for this unique group of patients.
A risk score is derived from a set of data by multivariate analysis by using C statistics, a measure of how well a clinical prediction can correctly rank-order pts by risk. A model that accurately discriminates pts 85% of the time has a C statistic of 0.85. A complete random statistic would be 0.50.
The third slide shows various ROC curves; the blue line has a C statistic of 0.5, i.e. it is no better than complete random order. Whereas the dark blue line has a C statistic of 0.85 and thus it is of clinical value to predict the patients' risk.
C statistics below 0.7 are only of limited or no value.
Although 20 000 consecutive pts were recruited for the data base of the EuroScore with 97 risk factors assessed, this was not sufficient to predict 1-year mortality. For both transfemoral and transapical cases the 1-year prediction was not much better than random statistics.
For the Source Registry, the C-statistics were slightly better, but not adequate for clinical purposes.
Risk Factors not assessed:
Frailty. Is there an explanation for the failure to create a clinical useful risk score from the data. There are several important risk factors which have not been included in the SOURCE registry. One of them is frailty which plays an important role in this particular patient population. Several frailty scores have been designed in recent years.
Causes of Death:
Another explanation for the failure of the C-statistics to reach a significant level is the great number of deaths that are not covered by appropriate variables, such as sudden death, cancer, stroke, and gastrointestinal.